Machine Learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed (definition of Arthur Samuel). Data Mining can be defined as the process that starting from apparently unstructured data tries to extract knowledge and/or unknown interesting patterns. During this process machine Learning algorithms are used.
The terms are often used synonymously. And as this is the case, it may be useless to distinguish them, as the answer will depend on the person who answers. My answer is:
Machine learning (ML) is more related to the question, how machines can learn, i.e., to the algorithmic part (how to learn from data, rewards, etc). An example is reinforcement learning that belongs to ML, but not to data mining (DM). An agent learns from rewards in the environment (which is of course also data), but not from patterns or pattern-label pairs. In DM, the question is how to learn from patterns or pattern-label pairs. New questions arise that may not be answered in the algorithmically oriented ML perspective including preprocessing of data and the complete DM process chain.
Machine Learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed (definition of Arthur Samuel). Data Mining can be defined as the process that starting from apparently unstructured data tries to extract knowledge and/or unknown interesting patterns. During this process machine Learning algorithms are used.
To augment to what Giovanni mentioned, Machine Learning (ML) techniques are fairly generic and can be applied in various settings. Data Mining (DM) has emphasis on utilizing data from a domain e.g., social media, sensor data, video streams, etc., to understand some questions in that domain. While DM may utilize machine learning techniques, it may also drive the advancement of ML techniques/algorithms. To utilize ML algorithms one has to formulate the problem in their domain to what ML expects -- usually a set of features. Once you abstract out the problem into features, you have access to a wide variety and rich set of ML algorithms.
Knowledge Discovery is the complex proccess of searching for not obvious and usefull knowledge (that is, models, patterns, rules, etc.) inside data.
Data Mining is a step of the Knowledge Discovery process which consists of the application of a machine learning algorithm to the data in sonsideration.
Some of machine learning algorithms can be successfully used in the process of data mining. Data mining consist of a data pre-processing phase, a phase of machine learning algorithm using and a phase of acquired knowledge interpretation. The condition of some machine learning algorithm using is creating of a labelled training set within the process of data pre-processing phase. The training set can be unlabelled, when we are attending to use some clustering method.
well, they are completely different concepts. Data Mining (DM) is the exploration of data searching for unknown properties and correlation laws (that's why data mining is a synonymous of Knowledge Discovery in Databases).
Machine Learning (ML) is a technique to solve optimization problems based on self-adaptive algorithms, by learning from known examples (supervised paradigm) or by self-organizing data (unsupervised paradigm).
DM could make use of ML. In particular clustering problems, based on the unsupervised ML paradigm, are a typical case of DM, since you train a machine to search unpredictable overdensities of data entries in the parameter space, without any a priori assumption. So far, by doing so, you can discover the unknown...
A agree more with Gionvanni di Orio. ML are techniques allowing computers to learn how to decide something. DM is a problem that can be solves by ML, but also by other techniques. ML can solve data mining but can also solve other problems like adaptive control, for example. I would say that ML are techniques and algorithms, while DM is an application.
Both terms largely overlap, but the emphasis is different. E.g. pattern mining & association rule mining is usually more associated with data mining than machine learning, while supervised learning is more actively researched in the domain of machine learning.
So in DM we try to get insight in the data without necessarily getting better at a task (e.g. we detect a community without optimizing for being able to predict community membership). In theory, machine learning is also possible without data, e.g. in reinforcementlearning (where the machine collects the data itself) or self-play in learning to play a game.
I would place Machine Learning as a sub-functin of the Data Mining process. Data minig as per the CRISP-DM model is a six step process composed of problem understanding, data understanding, data preparation, model development, evaluation/interpretation, and finally deployment. Machine learning focuses mostly on the model development phase. One can also say that Machine Learning is more concerned about the development of computational learning theory and implementations that test this theory; where as data mining is more about the use/application of well understood machine learning methods.
Just to add few things, in ML we are concern with 1) Design of Algo. to learn automatically from Data 2) Estimate parameters that gives best results on given dataset ( solving Optimization problems) 2) Issues of Generalization, Over fitting and Under fitting 3) Improving the accuracy of models etc.
In DM we accept the algorithms (given by ML) as it is and apply them on real time data of reasonably large size (scalability, data pre-processing, representation of data, visualization etc. are the matter of concern).
So to me ML is theoretical aspect of data-driven learning while DM is practical aspect. but they are interleaved with each other, as new development in theory gives rise to new applications and new applications also open up new challenges to existing theory (its like applied vs theoretical). So "Data Mining => A Practical Machine Learning".
Data mining refers to methods which are explore the knowledge from data. However, the machine learning techniques deal with the learning part in artificial intelligent (AI).
ML is inductive Inference and complete consistent to the seen data. DM is some sort of "soft" inductive learning and does not claim complete consistency with the seen data.
I am not impressed with the answers above so let us be CRYSTAL CLEAR.
SHORT VERSION
PURE DM = discovery of previously unknown and reasonably un-forseen information that is used by humans.
PURE ML = internal representation of data/behavior which is used to achieve a goal like a human does.
LONG VERSION...
MACHINE LEARNING (TECHNICAL)
Machine Learning is a branch of A.I. with the following stages in its growth as a discipline: (i) mimic the human or animal ability to learn, and (ii) matching or exceeding human or animal learning by generalization/abstraction. There are 3 major common types in practical use: (i) Supervised (e.g. latent semantic analysis for classification), (ii) Unsupervised (kohonen neural network for clustering), and (iii) Evolutionary (e.g. genetic algorithms to generate alternative combinations which converge to a solution over time).
MACHINE LEARNING (NON-TECHNICAL)
You are tired of walking your dog, so you want to build a robot that can take your dog for a walk. You decide to build a machine that copies you. Every motion you make it records. You even pretend to fall down and get back up. You walk your dog and the machine observes your motions. You do this at the park, at home, at your friends house, giving lots and lots of examples to the observing robot. Later, you give the robot a map, and it takes your dog for a walk, replicating your motions. In summary, you took data D, constructed a mathematical model M which represents D in a black box, and then you use this M to execute actions or make decisions for you while you are sleeping. At the first stage, in this "supervised learning" example, you merely trained a machine, with lots of examples, to copy you. At the next stage of evolution, you would expect this robot to run the marathon without you showing it how to do so.
DATA MINING (TECHNICAL)
Data Mining, which is what Data Science has become due to the mis-use of what data mining means (discussed later), is a process by which previously *unknown* or *hidden* or "unstructured* information, is extracted by *quantitive processes*, from *big data*, which is understood in its **transformed state** by humans (e.g. PCA analysis), for the purpose of making decisions, feature engineering, etc. The process to perform "real data mining" involves techniques including, but not limited to, Machine Learning (e.g. clustering of data to find new patterns) or Applied Mathematics (e.g. Statistical distributions, high dimensional regression trends, such as in predictive analytics, etc).
DATA MINING (NON-TECHNICAL)
You take water and put it on a camp fire, knowing nothing about what will happen previously, observe that something rises out of the bowl, which you call steam. The steam, which you knew nothing about previously, is your hidden data. You try similar experiments in different ways and you always observe steam... which is now your hidden pattern. So you took RAW DATA (i.e. water), applied a PROCESS (i.e. heat), and then discovered a new ouptut PATTERN (i.e. steam). In summary, you took data D, applied math functions on D, to extract a new transformed discovery T, which is then displayed to you in a presentation, with visual plots, with message lines, stating conclusions, which you as a human can use to make smart future decisions. For example, you can now predict, that if you place water on top of fire you will get steam. Of course you would then continue such processes until you understand how this magic could have happened going deeper and deeper and deeper in the chemistry.
Data mining is often mis-used as a combination of separate words "data" (i.e. there is data somewhere") and "mining" (i.e. as if i am going to walk over to this mine, and shovel up gold and put it in a truck and move it somewhere).
For clarity, Data Mining has nothing to do with "storing data", "fetching, searching, getting or retrieving data", "moving the data around" or "displaying this data in it's original format visually". That falls into the category of "databases", "programming" or "software engineering".
NOTE
Both ML and DM are highly specialized at the doctoral and post-doctoral level, in quantitative disciplines only (e.g. Theoretical Computer Science, Math, Physics, Computational BIology, Electrical Engineering, etc).
Machine Learning and Data Mining overlap each other and the difference is seen only in the application context. Some stuff like visualization, Data base sizes and Data base characteristics are considered only in Data mining tasks, while some concepts like reinforcement learning are considered only in machine learning.
Once more, the key difference between inductive inference (a subfield of machine learning) and Data Mining is the issue of being 100% consistent with the data or making a model (dcision tree, rule set, ... whatever), which accepts to be not 100 % in line with the given data (that may have outlyers and wrong items), but may be even better than ML due to the problems with the data.
Hello.
Machine learning (ML) is a term concerning many different algorithms which are aimed to 'mimics' learning. ML is a part of DM as a process (ML algorithms are used to analyze data), and DM is a part of KDD. You can realize what is the difference between ML and DM on Fig. 3 (http://www.dataprix.com/es/el-modelo-referencia-crisp-dm) in CRISP-DM (Cross-Industry Standard for Data Mining): DM is entire, multi-step process, ML are certain algorithms, and are used only in Modelling phase. The widest term is Knowledge Discovery from Data in which DM process (with ML algorithms of course) is used as well as visualisation, interpretation and conclusion drawing (hypotheses checking).
Machine Learning is a tool for Knowledge Discovery through Data Mining.
The primary difference between machine learning(ML) and data mining(DM) can be stated in their application. ML comprises of algorithms to help various systems learn and replicate any other natural system. In this process data is used to train the system and help it acquaint with the environment of the natural system that it is going to mimic. DM on the other hand purely works on the concept of finding new types of patterns in a given dataset. There is no learning involved here.
Data mining means to retrieve useful information from data with respect to a data model. Machine learning seeks to identify behavior patterns in data, and them build various models based on observed patterns.
Data mining means to retrieve useful information from data with respect to a data model. Machine learning seeks to identify behavior patterns in data, and them build various models based on observed patterns.
Machine Learning (ML) points to a toolset (a set of algorithms and computational infrastructures) . ML is more of a FIELD in Computer Science, created to fill the need of providing mathematical tools to automatically MAKE SENSE of DATA. It includes a set of algorithms that have been very well studied with a solid theoretical backing. Examples are ANN (Artificial Neural Network), SVM (support vector machines), k-means clustering, Naive Bayes, Linear regression ... The field of ML is using whatever mathematical knowledge we have available to provide ways to ANALYZE, CLASSIFY, and MAKE SENSE of data. This is what a human brain does (and, does VERY WELL). ML is located right inside the Artificial Intelligence (AI) field.
Remember, the field of AI, or ML were created before we barely knew what a computer was ! This is 60's, possibly earlier. The broader impact of AI and ML go far beyond computer science. I have seen colleagues use AI and ML techniques in biomedical research. medical research, definitely image processing, electrical engineering in general, and about half the computer science fields, if not more.
So, what about DATA MINING ? Well, when ML was becoming popular, A LOT OF DATA meant Kilobytes in the 60's. Now, My smartphone has Gigabytes, and internet allows us to process over zetabytes per year. Now, there is a need to have tools that allow us to make sense of data that are 5, 6 orders of magnitude higher in volume. Good news is that, all of the AI and ML techniques can do that, provided that, I have fast enough processors and a cloud infrastructure ... And, I do ! So, we use many of the existing ML techniques to provide Data Mining algorithms to answer questions like : If this customer bought milk and cookies today, how likely is (s)he to buy bottled water within the next three days ?
To give a brief answer, data mining can be considered as a phase/field in machine learning. Since data mining is the process of extracting useful information from raw data (which is strictly based on application) . The next step toward helping the machine learn, is to construct an appropriate model on the gathered information. The whole process is machine learning!
The difference between ML, DM, KD and Statistics is fuzzy. Techniques like Decision Trees are routinely used in all these contexts. I like the answer of Mahnaz Behroozi for its double entendre. DM is a phase in ML.
1. DM is a recent approach to finding patterns in large datasets, and developed independently of machine learning and statistics, often using similar terminology and notation in dramatically different ways, creating great confusion. In particular often the sense of union and intersection are interchanged in that DM thinks in terms of the rules and ML/stats in terms of the interpretations or instances. This is because more rules being postulated constrains the interpretation to be smaller (the number of instances satisfying all the rules).
2. DM can be viewed as a temporal phase in the development of machine learning and statistical learning, as a new application area has emerged, and ad hoc techniques are gradually being usurped by older techniques with a long pedigree in learning theory or statistics. It also tends to contrast with the more common supervised learning techniques, being more unsupervised in nature.
3. DM can be viewed as a temporal phase in machine learning in the diachronic sense that we have developed algorithms in a theoretical or toy world context, that are now being applied to massive datasets. Similarly in a synchronic sense, we can think of the work we are doing on learning algorithms as machine learning, and the phase spent analysing data as data mining.
Dear Souhila Sadeg,
In the process of Data mining the useful information is to be retrieved from database with respect to a data model (logical model). Whereas, Machine learning needs to identify behavior patterns in data, and them build various models based on observed patterns that is (probabilistic model).
It seems to me that the comments offered by Dr. Powers have articulated the matter well. The emphasis on logical set reduction concretely differentiates the processes that would occur within what he defines to be the data mining method. (To a student, at least, this is slightly more helpful for reducing ambiguity than general ideas as to what people might try to accomplish with one method versus the other).
If I have interpreted literature correctly, machine learning approaches are primarily characterized by two functions: human classification of the unstructured data instances (the “learning set”), and then the separate function of analyzing the data/document’s characteristics. Based on patterns of document characteristics which tend to be associated with humans assigning a particular meaning, the program can “learn” to recognize how humans would identify a thing.
Is it correct, then, to say that machine learning would be considered useful when a set of encoded rules are not already known or available for further interpretation? ( The question is, perhaps, more relative to the matter of supervised learning in particular.)
Blunt, short, therefore simplified answer:
Data mining is the objective, machine learning is the tool.
Similar to the answear of @Berry de Bruijn, data mining ist the objective, so what do you want to mine within the data. For this objective your are using mathematical (mostly probabilistic) models, such as maschine learning algorithms.
Maschine Learning is just an algorithm approach. It is just a method to do something on specific way. The most mashine learning algorithms try learn from human decisions and classification to do them in similar use-cases automatically.
Mr. Berry's answer is very much up to the mark. Data mining in simple terms means mining data for some purpose from unstructured data. Machine learning on other hand is training machine to do something by creating models.
@Berry, Dirk and Hemalatha: Partly true...
Data Mining is traditionally about existing data.
Machine Learning is traditionally about learning.
Data Mining used to be primarily unsupervised.
Machine Learning tended to be more supervised.
Knowledge Discovery and Unsupervised Learning and Inductive Science are about discovering new patterns, forming rules or models or theories, and developing parsimonious explanations - including of course the various methods and/or algorithms required to achieve this.
Machine Learning is actually often about achieving specific objectives, often hand in hand with an experimental paradigm and/or data collection and annotation protocols - that is to develop the very necessary and often expensive datasets. Machine Learning is often more about the paradigm than the algorithm. Any supervised algorithm can be applied in an unsupervised paradigm, and conversely unsupervised algorithms can be applied to learn a desired set of outcomes or labels.
To use a supervised algorithm in an unsupervised paradigm, use part (or all) of the data to predict part (or all) of the data. Conversely it can be useful to use unsupervised algorithms to prestructure data or for feature extraction in a supervised or semisupervised context, or just do the clustering or extract the rules and match against the target outcomes. This also relates to the ideas of deep learning, fusion, boosting and other layer-like and ensemble-like techniques
Another very important part of the equation is how to evaluate, which is something we don't know how do well for either supervised or unsupervised learning (and a major research interest of mine). A closely related concern is dealing with bias and noise in the learning, the raw data or the annotation, as well as in dealing with big data, including very high dimensional input spaces. These issues require techniques that cross the traditional boundaries between Data Mining, Machine Learning and Statistics.
There are at least 3 major types of problems that one can tackle:
A) class comparison
B) class prediction
C) class discovery
In a class comparison problem, the classes are given (e.g. a group of cancer samples and a group of healthy samples - we already know the class for each sample). The goal here is to find all the important differences between the two classes.
In a class prediction problem, the classes are also given but the goal here is to build a device (a classifier) that can assign the correct class to a previously unseen sample. In principle, building a classifier requires a feature selection stage that is similar to a class comparison problem so one could think that prediction is more difficult since you have to do class comparison anyway and then build a classifier. However, the goals are different. Class comparison aims to find _all_ differences which is more challenging. Class prediction would be happy to find one single feature that can differentiate well between the classes which could be easier.
In a class discovery problem, the classes are not known in advance. The goal is to identify subgroups of samples that share certain features, subgroups that are more homogeneous than others.
A simple answer to the question above would be to say that machine learning methods (aka supervised learning methods) are tools that can address class prediction problems while data mining are tools designed mostly for class discovery and sometimes for class comparison.
Some people include unsupervised methods, such as clustering, in the machine learning category. Unsupervised method are the preferred methods in class discovery problems.
Data Mining is a very basic discipline that encompasses many different methods to build models, estimate parameters to instantiate the model and use then use this later model to predict. Machine learning is but one facet of Data mining.
Machine learning essentially deals with algorithm and its development. It is a means to model the behaviour of a phenomenon or a process. It could be automatic or human directed.
Data mining essentially meant for delineating the relationships hidden in the data and beyond principal component analyses. DM brings out inherent trends, associations and relationships from a pool of data.
In data mining, one uses machine learning techniques to discover knowledge in databases
Data mining is about knowledge discovery in huge volumes of data structured or non-structured using various simple or complex algorithms, machine learning is one of the many complex algorithms used in data mining
See: Gorunescu, F., Data Mining, Concepts, Models and Techniques, Springer-Verlag Berlin Heidelberg (Series Intelligent Systems Reference Library)
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-19720-8
SEE THIS LINK
www.cs.swarthmore.edu/~eeaton/teaching/cs63/.../ML-DecisionTrees.ppt
Any computer system that is able to predict, associate, classify, etc., with intelligent behavior can be considered a machine learning system. Moreover due to the large volume of information in a database, it was necessary to create a field that would allow from these data, which may be structured or unstructured, extract knowledge difficult to be detected by a human being at first sight. This knowledge can be gained by performing tasks like prediction, classification and association; is for this reason that one of the phases of the data mining techniques that are common in automatic learning are used.
these techniques may be artificial intelligence algorithms, statistical or other branches.
Being more specific!!!!!!! data mining uses machine learning to extract information, as you can see the concept used by google news , that uses clustering algorithms to separate different types of news......so we can say that data mining is loosely an application of machine learning.
See this interesting lecture given by Pedro Domingos, one of the more influent researcher in the field, in the course "CSEP 546, Spring 2012 Data Mining". In the lecture the instructor give the difference between Machine Learning and Data Mining.
Link: http://courses.cs.washington.edu/courses/csep546/12sp/video/archive/html5/video.html?id=csep546_12sp_1
Hi Souhila,
I also wonder this quite frequently. Many descriptions can be very ambiguous.
I see it like this.
You use machine learning when you know specifically what you want to extract from your data. That could be predicting stock prices, sales, air passenger demand or anything.
You use data mining when you don't know what you want; when you want the computer to show you something new or interesting about your data. Some clustering algorithms fit into this area. They take a data set and automatically find clusters. But you don't specify what the computer should extract. The algorithm is only searching for an interesting structure in the data.
Data Mining usually goes only as far as interpreting the data. It is a part of Machine Learning that is given raw data, and then, using Machine Learning methods, extracts some meaningful information about it.
Machine Learning in general can have more steps than just interpreting the data. Programs developed Machine Learning techniques can also act upon the knowledge "learned" from the data, e.g. a program that is given a bunch of examples of Checkers games and based on that is able to play the game (well), has "learned" from the examples -- the data, and can now interpret new (similar data) and act upon that
Actually they are complementary.. in data mining you use ML to extract information from vast amount of raw data and make interpretation of that data. while in ML it is not the only task to interpret it also includes developing new methodology and improvement of exciting methods..
Both are the same thing but with different focus. But data mining is more about utilizing of machine learning findings while Machine learning covers the research & development efforts. So applied thinking they talk about data mining, research wise thinking they talk about machine learning.
Machine learning community focuses on theories, while data mining community pays more on algorithms and applications
Use data mining to get rules from data.
with machine learning, you teach you computer to learn and understand your rules.
To simplify, I usually say I use machine learning tools & algorithms in my data mining projects.
There other operations and technologies involved in DM, and machine learning algorithms are used in a variety of fields apart from DM.
Just a small addition to Pramod's answer, ML is the methodologies that are used to allow computers do intelligent task as human do; prediction, detection, classification, recognition, etc. While DM is a research area that uses methods such as ML to extract valuable information from data; like association rules, clustering, customer interests, etc. to make it short, ML is the methods and DM is the applications of such methods to get benifets from data in any domain, Business, Bioinformatics, social networks and so on.
Hope this help!
Hai machine learning is entirely different with data mining. Machine learning is to read the machine and data mining is to extract data from any data warehouses. Machine learning relates to system software but data mining is to mine the data from data ware houses i think in one way these two are interrelated i.e., machine learning is to learn the previous experience from knowledge mining. We are using some knowledge mining extraction algorithms.
Data mining is extracting hidden patterns of large data. Machine learning can be a tool for it, using different computerized algorithms.
The main difference is in vocabulary! :) ML and DM are typically very similar or even the same, with the DM community focusing a bit more on scalability and efficiency. And while we're at it, information retrieval (IR) is not much different either.
The only ML subfield that I would consider very different is that of Reinforcement Learning becauses this one really is about having machines learn to do certain tasks.
Data mining and machine learning used to be two cousins. Though the roots of both the concept are different but in later stage both are functioning towards the similar lines. The field of machine learning grew out of the effort of building AI. Its major concern is making a machine learn and adapt to new information.
The field of data mining grows out of knowledge discovery from databases. Data Miners typically have strong foundation in machine learning, but also have a keen interesting in applying it large-scale problems.
Data Mining uses Machine Learning algorithms to mine big datasets.
@Pawel: I'd argue that data exploration is one way of using data mining results.
To give an example, predictive modeling uses data mining results to build a complete model on labeled data to predict labels for other data points for which one doesn't have labels yet.
But to select how to model the data, it helps a lot if one understands the data first. So what one could do is using clustering techniques to get an idea how data points are distributed, co-occurrence mining of all kind to identify attribute value combinations that always/often co-occur (maybe to merge them into single attributes), outlier detection techniques to see whether there are points in the data that are unlikely to be produced by the same generative processes as the rest (since modeling those might take a lot of effort, or might distort the model), subgroup discovery to gain an understanding of what values characterize sub-populations the distribution over labels of which differs from that of the full data.
The difference in this case is NOT in the techniques but in what to do with the results. In fact, with a bit of creativity (not very much, honestly), one can use a full clustering of the data, a collection of subgroup descriptions that cover the entire data, or patterns that indicate the co-occurrence of labels and other attribute values with each to do the predictive modeling step.
In general machine learning has to do with dealing with uncertainty through experience. This notion tends to create new AI based algorithms, where AI is a greater umbrella where machine learning residences. Data mining on the other hand has to do with the application of these algorithms to the specific case of data exploration and exploitation in order to understand the relations between the data while in advance make predictions. In could be drawn that data mining help unstructured data to reveal their inner information.
I really like Giovanni's answer above and I think it is the most accurate and general at the same time(!). I would add to the discussion in this way: The terminologies associated with this field (which encompasses machine learning, data mining, big data, etc.), unfortunately suffer from differences in interpretation from stakeholder to stakeholder. Statisticians tend to have different definitions than those of us coming from either computer science or computer engineering, for example. What's more confusing, **many** different industries and sectors are just now getting interested in these technologies, literally as we speak. The waters get muddied quite a bit in job postings, for example, when hiring managers, marketing managers, etc., jump on the bandwagon and do not truly know exactly what they are asking for(!). I've found that as a new PhD hunting for jobs, I often have to gently "educate" my interviewer to get them to ask me the right questions, refine what they are looking for, etc. I would say, be prepared for this and get ready to aid your audiences if needed.
I would stick with Giovannis answer. Data mining also can be divided into "predicitive" and "descriptive" techniques. Predicitve techniques lean towards machine learning where a model learns over given data (training and testing) and then predicts new instances of data using the learned model e.g. classifiers like decision trees, Bayesian, neural nets etc. Descriptive techniques merely describe characteristics/patterns in the data (they dont need to learn, though they can be incremental, meaning if you end up with a rule base, you can always update it with new data/rules). Examples include association rules, summarisation/generalisation/inductive algorithms etc. With data mining, you have an overaching approach called knowledge discovery which starts with finding data, extracting it, transforming it, validating/cleaning it etc, applying data mining/machine learning, then finding patterns/models, and evaluating how interesting these are to help make decisions on the patterns. Datawarehousing is just a store of validated/cleaned and highly dimensional historical data (obtained from daily transactional data, called online transactional processing - OLTP) of an organisation to help perform advanced data analytics/aggregations/summarisations called online analytical processing-OLAP) etc. To load data into a warehouse, you use extract transform load tools out there (loads of them) - ETL tools, and perform data summarisation according to which ever subject/dimension you choose....
At simple view they are both look the same, for example the same algorithms are used in both data mining and machine learning fields but the differences appear when you look at their use. the objectives in these fields are as follow:
The detailed objective of machine learning are mentioned above by another answers but in summary, in machine learning we try to increase the accuracy of the algorithms and we assumed that the data that we provide for an algorithm are exist and clean. in other words we cannot make effort on gathering data, clean it or pre-process it, we just focus on increasing the accuracy!
The objective of data mining are different from machine learning from practical point of view. As mentioned above we dont make effort for gathering or managing or preprocessing data in machine learning but the 80% of efforts and costs in data mining or data science projects are about these topics, after doing these efforts what we will do are the same as machine learning process. Another difference is,in data mining projects first of all we look at machine learning algorithms that are applicable and scalable to huge and massive datasets without considering accuracy. In other words in data mining projects applicability and scalability are more important than accuracy, accuracy is a second matter.
Thank you
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Dear Souhila, I agree with Giovanni Di Orio about the accepted differences between Machine Learning and Data Mining. In a general sense, the last (DM) concerns several methods to statistical analyze row data which presumably carry some unknown information hidden into them. Machine learning (such as Neural Nets, supervised-unsupervised Cluster analysis) is one of the possible tools to be used by DM algorithms.
Machine learning is a general concept that developed as a new capability for computers. Anyone who uses computer systems want the machines to work intelligently. To accomplish this, Machine Learning grew out of the work in AI. Autonomous Helicopter, Handwriting recognition, Computer Vision are various applications beyond the programming capability. So ML comes to our rescue. One such application is Data Mining. Data mining seeks relevant information from huge amount of heterogeneous or homogeneous data sources for understanding the data and/or predicting the consequences. Data mining is a specific concept that grew out of the work in Databases and tries to explore the data which may be structured or unstructured. Machine Learning comes to the rescue of Data Mining for knowledge discovery.
see dear
data mining is the technique to extract hidden knowledge or information from data.
One of them is machine learning. Its an artificial intelligence method.
Machine learning is cluster or classify data but data mining is preprocessing data before classification or clustering
In data mining you are trying to get information from dataset for analysis. With machine learning you are using dataset to train machine and applying learnt things on new things.
These two terms overlap significantly :
- In machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge,
- In Data Mining the key task is the discovery of previously unknown knowledge.
Data Mining is often considered a sub-field of Machine Learning.
Data Mining usually goes only as far as interpreting the data while Machine Learning in general can have more steps than just interpreting the data.
Machine learning is the process of Prediction while data mining mean only extraction of knowledge
Machine learning includes methods (algorithms ) to make computer (can do computations at much faster rate) find connection (correlations) between data and the knowledge/pattern we are trying to extract (supervised learning) from it or are interested in prediction when the outcome of the underlying process is not observed/observable (unsupervised learning).
Data mining includes everything (cleaning, storing, retrieving, automating and performing machine learning and statistical tasks, creating visualization tools to look at the data from different perspectives) we do to turn raw data into useful insights and information.
Artificial Intelligence is the field of Informatics which studies how we can teach a machine to act (i.e., not to think!) as a human. Machine Learning is the subfield of Artificial Intelligence which specializes in teaching a machine through its prior experience. Data Mining is a subfield of Machine Learning which learns the machine how to learn within a specific domain of knowledge.
Data Mining an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
Machine Learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed.
Data Mining VS Machine learning
Data Mining:
Algorithms to search for patterns and relationships that may exist in large Databases
Because data sets are so large, many relationships are possible. To search this space of possibilities, machine learning techniques are used.
“Correct” use of term data mining is that it is part of process concerned with finding patterns in data.
In industry, data mining is often used for the whole process
Machine Learning:
“Computer program that improves its performance at some task through experience.”
“A learning system uses sample data to generate an updated basis for improved [performance] on subsequent data from the same source and expresses the new basis in intelligible symbolic form.”
“Learning denotes changes in the systems that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more effectively the next time.”
To quote my philosophy professor: "What is" questions are dangerous (in the sense that they often rather confuse than enlighten). The problem is that "what is" questions usually root in a philosophical paradigm called "essentialism", that is, (according to Wikipedia) "the view that for any specific entity (such as an animal, a group of people, a physical object, a concept), there is a set of attributes which are necessary to its identity and function." This set of attributes is then seen as the "essence" of that entity (hence the name "essentialism"). In the question here this view is applied to the concepts "machine learning" and "data mining", hoping that the answers will somehow reveal the "essence" of these concepts. I don't think this makes much sense. There is no "essence" to these concepts, especially, since both terms have been buzzwords for quite some time, which tends to water down a word's meaning until almost nothing tangible is left. The two words in question here are actually often used as synonyms, despite the effort some people make to distinguish them by something that usually sounds like hairsplitting to me (like machine learning focuses on the learning or model aspect, data mining on the large data volume aspect). I recommend to read Karl Popper's criticism of essentialism and his argument for nominalism, which should quickly deter you from asking such questions, especially w.r.t. buzzwords. There is usually very little to be learned from the answers.
Data Mining refers to a compendium of tools, where machine learning techniques are one of them. Machine learning only refers to those non parametric techniques where the solution is reached by means of a "learningn" process which could be statistical or heuristic, amontg others
To add to Christian's answer, buzzwords tend to distort fundamental concepts than expound them. For example, "knowledge discovery (KD)" buzzword came about when data mining was somehow referring to algorithm/model development and application. Knowledge discovery is a process- from data to patterns (with data mining/machine learning (ML) algorithms or tools applied to the data). Data mining algorithms don't have to "learn" by building a model, they can be applied directly e.g. association rule mining. When we start to "learn" by building models to find patterns, then "pattern recognition" deserves a shout too! Some how, the KD framework has been evolving and now we hear of the CRISP-DM (CRoss Industry Standard Process for Data Mining) model, for carrying out data mining projects. Like as if we haven't had enough of them buzzwords, "Business Intelligence" is used nowadays in business to cover "all these buzzwords" so that an organisations data is turned into patterns that help make intelligent business decisions (whether its a process used, or data mining/ML algorithm or CRISP model etc), take your pick!
ML provides algorithms that resolve the task based on the data, and the solution improves with time. Ex: Assigning documents to a folder
DM extracting regularities from a very large database as part of a business or application. Ex:prediction of aircraft component failures
Data Mining usually goes only as far as interpreting the data (e.g. categorizing newspaper articles based on their theme, or books according to the suitable age of readers). It is a part of Machine Learning that is given raw data (real data), and then, using Machine Learning methods, extracts some meaningful information about it. Data mining relies on real data. This data is extremely vulnerable to co-linearity precisely because data from the real world may have unknown interrelations.
Machine Learning in general can have more steps than just interpreting the data. Programs developed Machine Learning techniques can also act upon the knowledge "learned" from the data, e.g. a program that is given a bunch of examples of Checkers games and based on that is able to play the game (well), has "learned" from the examples -- the data, and can now interpret new (similar data) and act upon that. machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.
Machine learning is a branch of artificial intelligence, concerns the construction and study of systems that can learn from data, On the other hand Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science.
Machine learning is also used in intelligent systems. For example, in hyper-heuristic an algorithm optimisation process would assess the quality of each algorithm from their performance of solving the problem. This constitute the knowledge. Then it builds up a better algorithm solutions for an specific problem.
In Data Mining, specific information are selected from a high volume of data. It focuses more on databases techniques or others algorithms produced by machine learning.
IMHO, the differences are in the goal. Roughly speaking with simple words :
The goal of Data Mining is related to extract relevant data in a larger set of data containing both relevant and irrelevant data.
The goal of machine learning is, it its name says, to automatically learn to do something : Data Mining, Classification, ...
My mere opinion is that the difference between ML and DM consists in WHAT they aim to estimate.
Their common point is that both of them estimate SOMETHING from data, but:
1) ML algorithms aim to estimate the PARAMETERS of a MODEL which is used to do "something" (like classification, regression, predictions, etc.)
2) DM techniques aim to find CORRELATIONS existing in the data itself, and to find useful information which is hidden to the human eyes. A popular example is the "discovery" that, by putting diapers and beer close to each other in a supermarket shelves, the sells of these two producs increase.
An interesting pdf file explaining the difference:
http://web.cecs.pdx.edu/~mperkows/CLASS_479/LECTURES479/PE013..pdf
Data mining and machine learning used to be two cousins. They have different parents. Now they grow increasingly like each other, almost like twins. Many times people even call data mining by the name Machine learning.
The field of machine learning grew out of the effort of building artificial intelligence. Its major concern is making a machine learn and adapt to new information. The origin of machine learning can be traced back to 1957 when the perception model was invented. This is modeled after neurons in human brain. That prompted the development of neural network model, which flourished in late 1980s. From 1980s to 1990s, the decision tree method has become very popular, owing to the efficient package of C4.5. SVM was invented in mid-1990s and it has since been widely used in industry. Logistic regression, an old method in statistics, has seen growing adoption in machine learning after 2001 when the book on statistical learning (The Elements of Statistical Learning) was published.
The field of data mining grows out of knowledge discovery from databases. In 1993, a seminal paper by Rakesh Agrawal and two others proposed an efficient algorithm of mining association rules in large databases. This paper promoted many research papers on discovering frequent patterns and more efficient mining algorithms. The early work of data mining in 1990s was linked to creating better SQL statement and working with databases directly.
Data mining has its strong focus on working with industrial problems and getting practical solutions. Therefore it concerns with not only data size (large data), but also data processing speed (stream data). In addition, personalized recommended systems and network mining are all developed due to business need, outside the machine learning field.
The two major conferences for data mining are KDD (Knowledge Discovery and Data Mining) and ICDM (International Conference on Data Mining). The two major conferences for machine learning are ICML (International Conference on Machine Learning) and NIPS (Neural Information Processing Systems). Machine learning researchers attend both types of conferences. However, the data mining conferences have much stronger industrial link.
Data Miners typically have strong foundation in machine learning, but also have a keen interesting in applying it large-scale problems.
Over time, we will see deeper connection between data mining and machine learning. Could they become twins one day? Only time will tell.
As a scientist coming from other field (computational neuroscience) who works as a data scientist I feel the difference between data science and machine learning is like the difference between physics and engineering (computer science), respectively.
Let me elaborate on this, if I may: I see data science as a practice of researching data from arbitrary field and coming up with INSIGHTS, MODELS which help to UNDERSTAND it. This is similar to how physicist see their profession: they collect observations (experimental physics) and try to find laws of nature (theoretical physics) which can account for these observations.
By contrast, in Machine Learning, an engineer (computer scientists) has a more system and theoretical view on how things are DONE: what are the bounds of performance of a method, efficiency of algorithms, how to estimate parameters of a GIVEN model from data in the best way etc.
A toy example: say that you have observation of location (x) and forces applied (f) and you need to predict next location from past observation. a Machine learning approach would be to see {x(t-k)} and {f(t-k)} as features and learn a model x(t) = F({x(t-k)}, {f(t-k)}. A data scientist (physicist) will try to find a ground rule, such as the 2rd law of motion: d2x(t)/dt2 = m*f(t). Now there might be some deviation from theory (e.g. friction, wind etc) in which the ML approach will have better prediction than the data scientist model, but the latter has a better insight and understanding of the dynamics underlying the observations. Finally, a ML model can be based on the DS insight (e.g. 2nd Law of motion).
Machine learning and data mining are a bit similar, both can use any size of dataset what so ever small or large. Machine learning concentrates on the algorithm itself where data mining in addition to that has other processes to do such as analysis and statistics, etc.
Difference between machine learning and data mining are as follows;·
- Machine learning as a whole includes a very large variety of problems, such as supervise learning, unsupervised learning, clustering, attribute selection etc., While data mining is also addressed variety of problems but knowledge of statistic is very vital at each phase.
- In machine learning statistical model updated automatically while in data mining, data scientist need to run model multiple time for better results.
- In both (machine learning and predictive analysis) it is mandatory to clearly define business objective or business problem to address.
- Machine learning is data driven while data mining is use driven
- The data mining as a branch of the machine learning
Machine learning is a broad aspect of AI that entails subjecting a machine to learn from historical data without being explicitly programmed. It involves creation of models, methodologies, algorithms and techniques WHILE Data Mining is the ability to extract relevant data from an application or system to solve a particular problem. Data mining is done in any applications or domain.
Machine learning provides analytics tools to do data mining. Data mining is the mining process to extract information from data.
As Elmer says, ML -neural networks, genetic algorithms...- is one of the tools used in Data Mining, being other tools besides it for the same purpose. The choice of tools to use depends equally on the data available and on the purpose of the search.
Sometimes it's difficult to determine which ones to use -experience is a good help- and sometimes it's better to give a try to all tools available, if possible, to cover all outcomes.
In a database environment, the data mining searches for some patterns of information. It is how supermarkets develops their knowledge related to their market, to predict our shopping habits.
Machine learning provides tools to solves problems, not only in data mining but also in a more general context.
Data Mining is the more general concept. Different families of techniques can be applied to data to "mine" it. Machine learning (supervised, unsupervised, ...) is one family of data analysis techniques. Traditional statistics (regression analysis, principal components, factorial analysis, etc.) is another.
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