Often the more general terms ( large scale) data analysis and analytics-or, when referring to actual methods, artificial intelligence and machine learning-are more appropriate. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. It also is a buzzword and is frequently applied to any form of large-scale data or information processing ( collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The term 'data mining' is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction ( mining) of data itself. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the 'knowledge discovery in databases' process, or KDD. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.

Data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

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