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 in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. 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 in contrast, data mining uses machine-learning and statistical models to uncover clandestine or hidden patterns in a large volume of data. 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 process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.ĭata mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.ĭata mining is the analysis step of the “knowledge discovery in databases” process, or KDD. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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