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Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learningstatisticsand database systems.

The term "data mining" is a misnomerbecause the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction mining of data itself. The book Data mining: Practical machine learning tools Compputer techniques with Java [8] which covers mostly machine learning material was originally to be named just Practical machine learningand the term data mining was only added for marketing reasons.

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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 analysisunusual records anomaly detectionand dependencies association rule miningsequential pattern mining.

This usually involves using database techniques such as spatial indices. 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. 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.

Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The difference between data analysis and data mining Prrivacy that data analysis https://amazonia.fiocruz.br/scdp/blog/gregorys-punctuation-checker-tool/pros-and-cons-of-the-equal-pay.php used to test models and hypotheses on the dataset, e.

The related terms data dredgingdata fishingand 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. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

In the s, 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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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 The term data mining appeared around in the database community, generally with positive connotations. Other terms used include data archaeologyinformation harvestinginformation discoveryknowledge extractionetc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic KDD and this term became more popular in AI and machine learning community. Computer Ethics Term Paper Security Vs Privacy, the term data mining became more popular in the business and press communities. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, inUsama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding editor-in-chief.

The journal Data Mining and Knowledge Discovery is the primary research journal of the field. The manual extraction of patterns from data has occurred for centuries.]