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Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for meaningful patterns (knowledge). With advances in the process of data collection, 1990s saw an explosion in growth of data. This coupled with the stellar advances in computing technologies really spruced up "Data Mining".
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" [1] and "The science of extracting useful information from large data sets or databases" [2]. Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts.

Poll: Which of the following would you recommend as the best introductory book on data mining?
Data Mining: Concepts and Techniques - Han & Kamber
Data Preparation for Data Mining - Pyle
Introduction to Data Mining - Tan, Steinbach & Kumar
Principles of Data Mining - Hand, Mannila & Smyth
Machine Learning - Mitchell
The Elements of Statistical Learning - Hastie, Tibshirani & Friedman
Introduction to Business Data Mining - Olson & Shi
Predictive Data Mining: a practical guide - Weiss & Indurkhya
Other
Books are way too structured and expensive for me!
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Why is data mining needed ?

The two high-level primary goals of data mining in practice tend to be prediction and description. Prediction involves using some variables or fields in the database to predict unknown or future values of other variables of interest, and description focuses on finding human-interpretable patterns describing the data.
The goals of prediction and description can be achieved using a variety of particular data-mining methods like,

and many more.

What are the research and application challenges for data mining ?

 
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