Data mining, also referred to as “pattern recognition”, “knowledge discovery in databases”, and “machine learning”, is the process of extracting useful information (“gold”) from potentially large quantities of data (“rock”). It is considered part of the wider field of Artificial Intelligence. Mining can take many forms: classification, clustering, regression, association rule mining, density estimation, and feature extraction are some common techniques employed by data miners. Data mining also encompasses search engine techniques, such as LSI.
Data mining techniques are considered highly advanced (and indeed have a large amount of statistics, linear algebra, and artificial intelligence background behind them) with most practitioners possessing advanced degrees in Computer Science, Mathematics, or Statistics. Much of the cutting-edge research, while very powerful, is confined to the academic sphere, with no suitable implementations existing in the commercial world for quite some time in certain instances. Specific methods can become very detailed and involve a great deal of jargon, including terms such as “PCA”, “SVM”, “MSE”, “neural network”, “kNN”, “boosting”, and “SVD”. And these are the more common techniques in the field, to say nothing of the many exotic ones!
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