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Lovell indicates that the practice 'masquerades under a variety of aliases, ranging from 'experimentation' (positive) to 'fishing' or 'snooping' (negative). The term 'data mining' was used in a similarly critical way by economist Michael Lovell in an article published in the Review of Economic Studies in 1983. In the 1960s, statisticians and economists used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis.

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9.2 Proprietary data-mining software and applications.

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9.1 Free open-source data mining software and applications.These methods can, however, be used in creating new hypotheses to test against the larger data populations. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. This usually involves using database techniques such as spatial indices. The actual data mining task is the semi- automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records ( cluster analysis), unusual records ( anomaly detection), and dependencies ( association rule mining, sequential pattern mining). 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 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 of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the 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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