A very interesting question is debating the differences between machine learning and pattern recognition. Now it has been mentioned data mining. I would like to know how they are related to each others.
Data mining is the process of extracting useful information from a database or data warehouse, machine learning involves math and stat to make computers learn (algorithms), while pattern recognition is an application of ML. One can recognize pattern from a database using ML and if they mine the patterns it is data mining. This is my understanding. Hope it helps
Data mining is the process of extracting useful information from a database or data warehouse, machine learning involves math and stat to make computers learn (algorithms), while pattern recognition is an application of ML. One can recognize pattern from a database using ML and if they mine the patterns it is data mining. This is my understanding. Hope it helps
Hi
Data mining is the pipeline process, consists of data collection, data pre-processing, model learning and model selection. As i know data mining deal with all aforementioned process and cover various applications like web mining, image mining, text mining etc. but machine learning starts from model learning and focus on the learning algorithms and model selection, most of machine learning method comes form artificial intelligence background. pattern recognition also focus on learning method but it comes from statistical back ground, the Bayesian theory widely used in pattern recognition methods.
conclusion
data mining -> dealing with data to make knowledge
machine learning -> dealing with learning algorithms from artificial intelligence background.
pattern recognition-> dealing with learning algorithms from statistical background.
Data mining, image retrival, etc. are based on methods and approaches from the fields of Machine Learning and Pattern Recognition. The aim is to find useful information of all kinds in large data bases. Insofar, in the todays world Data mining is of course related to Big Data approaches.
Data Mining is a wrapper for a bunch of algorithms intended to use computers (Machines) to analyze data. Learning stands for tuning algorithm to data. The desired output of the analysis determines what you are doing: Pattern Recognition (output is binary in case of two classes), or Regression (output is continues variable), or Dependency Reconstruction (output is function from the given class) and so on. These are principal tasks but at now days a lot of different problems can be considered from Data Mining perspective.
Thank you very much for your answer. I confirm and extended my understanding of data mining.
Thank you
Pat
I believe the three terms are used more or less interchangeably nowadays. However, historically both machine learning and pattern recognition were more concerned about the theoretical underpinning for learning from data. Data mining, a later coined term, has been more practical. Most of data mining techniques were born in machine learning and pattern recognition literature, when adopted in data mining, the approach has been on "how to apply?" rather than "would it really learn?". Again, as mentioned earlier, the three terms are now used interchangeably. Thus, you can find topics that could be traced back to early machine learning literature in a paper that uses the term "data mining" and vice versa.
Data mining is finding hidden information i.e. knowledge from the large database. So most of the times in pattern recognition we need to implement data mining techniques. Machine learning is related to artificial intelligence.
- Data Mining : A process that combines many tools (data gathering, data processing, workflow design, feature extraction, feature engineering, machine learning, post-processing, evaluation, conclusion)
- Pattern recognition: domain specific edition of data mining (e.g. image data)
- Machine Learning : an essential tool that is used in data mining and pattern recognition
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