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Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets ("big data") involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.〔 The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.〔 Aside from the raw analysis step, it 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.〔 The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction (''mining'') of data itself. It also is a buzzword〔See e.g. (OKAIRP 2005 Fall Conference, Arizona State University ) (About.com: Datamining )〕 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, machine learning, and business intelligence. 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. 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 actual data mining task is the automatic or semi-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). 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 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. These methods can, however, be used in creating new hypotheses to test against the larger data populations. ==Etymology== In the 1960s, statisticians 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. The term "Data Mining" appeared around 1990 in the database community. For a short time in 1980s, a phrase "database mining"™, was used, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Workstation; researchers consequently turned to "data mining". Other terms used include Data Archaeology, Information Harvesting, Information Discovery, Knowledge Extraction, etc. Gregory Piatetsky-Shapiro coined the term "Knowledge Discovery in Databases" for the first workshop on the same topic ((KDD-1989) ) and this term became more popular in AI and Machine Learning Community. However, the term data mining became more popular in the business and press communities. Currently, Data Mining and Knowledge Discovery are used interchangeably. Since about 2007, "Predictive Analytics" and since 2011, "Data Science" terms were also used to describe this field. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「data mining」の詳細全文を読む スポンサード リンク
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