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I am new to this area.

In my image,

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.

user900168
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    You may be interested in this related question on CrossValidated: [What is the difference between data mining, statistics, machine learning and AI?](http://stats.stackexchange.com/q/5026/930) – chl Aug 18 '11 at 10:04
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    my answer to a nearly identical SO question: http://stackoverflow.com/questions/3417709/ir-vs-data-mining-vs-ml/3422904#3422904 – doug Aug 18 '11 at 10:38

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Also, Data Mining is often considered a sub-field of Machine Learning.

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, 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.

penelope
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  • Even though Data Mining is typically considered as a sub-field of ML, but this categorization in not always true. For instance, Association Rule mining is an example of Data mining in which classical machine learning techniques are not used. – Ramin Jun 11 '14 at 21:55
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    @Ramin I was trying to give a very high-level, generalized answer without going in to details. And I did that 3 years ago. And, as you will notice, I didn't say _always_ but rather _often_ and _usually_. I still believe that's how DM started -- from ML. But of course, as soon as is started developing on its own, it incorporated new techniques, from many different fields. Which is what happens -- fields evolve and then mix (often much slower than they should). I'm also sure the state-of-the art in Machine Learning today is not only using "classical machine learning techniques" any more either. – penelope Jun 13 '14 at 09:49
  • @penelope, you are right - the whole field has become so interdisciplinary that I am not sure what I just said is also completely true. – Ramin Jun 13 '14 at 19:54
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The terms are not overly strict in defintion, but basically I think what you're saying is correct. Machine learning involves algorithm identification and finessing, whereas data mining implies a more static algorithm that is applied to fixed data. The output of machine learning is information of course, but also new algorithms identified through the process. Data mining seeks to apply a pre-existing algorithm over data.

Pete855217
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