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Not to be confused with analytics, information extraction, or data analysis. Data mining is the computing process of discovering patterns in large data sets...

Not to be confused with analytics, information extraction, or data analysis. Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an essential process where intelligent methods are applied to extract data patterns. It is an interdisciplinary subfield data analysis methods in research pdf computer science.


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. Practical machine learning, and the term data mining was only added for marketing reasons.

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. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

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. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. AI and machine learning community.

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