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). 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. 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 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. 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. 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. 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 analysis step of the 'knowledge discovery in databases' process, or KDD. 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 dredging vs data mining