Nada M. Al Hakkak ,
In machine learning, classification is a supervised learning task, where a training data set with both the features (inputs) and labels (outputs) are fed to a learning algorithm to infer a predictive model. With such a model, it is possible to classify future observations.
Clustering, on the other hand, is a unsupervised learning task, where the training data is composed only of the features (inputs). That is, we do not know what are the labels. Such a technique is able to group multiple instances into several clusters to help us better understand the data.
Nonetheless, unsupervised and supervised learning can be used together in many different ways, such as semi-supervised learning, dynamic classifier selection, among others.
The objective of classification and clustering is similar., however its data analysis technique or scale is different. In Bayesian parametric classification example, consider you have three groups or samples/classes X1, X2, X3 (you may processes up to N classes).
Suppose each of these X1, X2, X3 sample data have 100 points (may be vectors or matrices), for parametric classification task you divide each of these samples into 5 equal parts (you can divide it in10 equal parts etc). Here we consider the first part 20-points from each class for training purposes and the remaining for testing to processes in 4 sample trials. Accordingly you get your combine accuracy results from each trial.
Clustering is non-parametric technique. Here a simple strategy is that each member of the group going to the relevant group according to the shortest distance. See clustering details here http://www.vldb.org/conf/2003/papers/S04P02.pdf
Classification is supervised machine learning techniques, while clustering is unsupervised machine learning. Both can used to predict the class of given data (i.e., process related to categorization).
http://www.differencebetween.net/technology/difference-between-clustering-and-classification/
In order to understand the difference between classification and clustering I will present an example. Let's say we have 10,000 pictures of different people and we want to create a machine learning model that can predict the age of a person based on a picture. For each picture we have the corresponding age number. We assume that the minimum age is 1 and the maximum 100.
We have three options:
1. We can use classification (supervised learning), where the input for training our model will be the pictures and the age numbers. We then have to split our data into two datasets, training and test datasets, with 7,000 and 3,000 pictures and their matching ages, respectively. After that, we can use any supervised learning algorithm to train our model. Let's say we used a Multi-Layer Perceptron Neural Network with gradient descent. The model will have to learn how to classify a picture into an age group (1-100). This means our model has to extract all the features of all the 100 different categories. Not an easy task for such a small dataset.
2. We can use clustering (unsupervised learning), where the input for training our model will be just the pictures. Also we will not need to split our data, we will have just one dataset with 10,000 pictures. We can use any unsupervised learning algorithm, let's say we chose K-means algorithm. This model will attempt to create clusters based on the features that were extracted from the pictures. This model could eventually create 100 different clusters but it would require a lot of time to train and we do not want wait that long.
3. We can use a hybrid model by combining unsupervised and supervised learning, where the input for training our model will be the pictures and the matching age numbers. We can use an unsupervised algorithm, K-means for example, to create clusters based on the features of our data. Let's say our model created 5 different clusters for 5 age groups (1-20, 21-40, 41-60, 61-80, 81-100). Then we can use a supervised algorithm, Multi-Layer Perceptron for instance, to train a model for each of the 5 age groups. With that being said, we will have 5 separate classification models which will have to classify pictures into 20 age groups (the 1st will have 1-20 ages, the 2nd will have 21-40 ages, etc.). Each of these classification models will be trained with the data of the corresponding cluster. We will still have to split the data for each cluster into training (e.g. 70% of the data) and test (e.g. 30% of the data) datasets. This is obviously a lot easier than having 100 different categories to choose from.
You might wonder: "Do we have to use a clustering algorithm to create a dataset for the 5 different age groups, while we can easily separate them ourselves?"
- Well, not all problems are the same, so you will not be able to do that for every problem. What happens when a new picture comes into play? Are you going to estimate the age and give it to the appropriate model? We might be able to do that for a few pictures but not for a few thousands.
Please note that all the numbers are simplified for the given example, there is no guarantee that the clustering algorithm will create 5 clusters. Also, we will probably need a bigger dataset in order to create a model that can predict the age based on a picture, with high accuracy.
If I made any mistakes please let me know and I will correct them. I hope this helps.
The classification problem can be solved using any clustering method. If the results obtained can be interpreted within the framework of a meaningful theory, then we are talking about classification (or typology). If we stop at the stage of a formally obtained result (even if there is a formal criterion), we usually talk about clustering. In any case, the work should define (or separate) these close concepts.
In classification, classes are specified while in clustering classes are learned (clusters).
A classifier has to predict what class an object belongs to given that it has other objects with their “correct” classes.
A clustering algorithm has to put objects into classes(clusters) minimizing some notion of distance between objects
1 way you can combine both is by clustering your data using some interesting combination of features ahead of time (lets say the combination is expensive to compute) . These cluster labels can then be used as an extra feature that a classification algorithm can use to possibly attain higher performance.
About hybrid methods, they exist and are called semi-supervised clustering (when some samples are labeled, see here https://arxiv.org/pdf/1307.0252.pdf for a light review), while on the classification side there are some co-trining techniques like this http://pages.cs.wisc.edu/~jerryzhu/pub/AERFAI06ssl.pdf . Surely, the hybridization of clustering and classification is an open research problem. Since clustering is an exploratory method for defining unseen concepts or classes in the data (or for defining labels for a set of objects) and classification methods search for rules for assigning samples to predefined classes (or for assigning labels to objects), hybridize them what does it mean? For example, start using clustering for defining knowledge and then using classifiers for assessing or modifying knowledge dynamically. Or what else have you in mind?
classification: It is the process of classifying the input instances based on their corresponding class labels.
whereas
clustering: it is the process of grouping the instances based on their similarity without the help of class labels.
yes, hybrid methods can be developed. For more details: Article A Hybrid Classification Framework Based on Clustering
The main difference between aggregation and classification is that aggregation is a non-supervisory educational method that aggregates similar cases based on features, while classification is a supervised learning technique that assigns pre-defined signs to cases based on features
classification is performed on labeled data (supervised approach) while clustering is the unsupervised approach.
Consider the case of semi-supervised learning wherein the data available only part of that is labeled. In that case, first clustering is done after that classification is performed to get better evaluation parameters (like accuracy). here clustering can be thought of as a data processing method, after which classification algorithm is applied.
When classifying, the image of a particular object refers to generalized class images. and when clustering, generalized class images are compared with each other and combined into clusters.
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