Editing Data mining (section)

==Background==
The manual extraction of patterns from [[data]] has occurred for centuries. Early methods of identifying patterns in data include [[Bayes' theorem]] (1700s) and [[regression analysis]] (1800s).<ref>{{Cite journal|last=Coenen|first=Frans|date=2011-02-07|title=Data mining: past, present and future|url=https://www.cambridge.org/core/product/identifier/S0269888910000378/type/journal_article|journal=The Knowledge Engineering Review|language=en|volume=26|issue=1|pages=25–29|doi=10.1017/S0269888910000378|s2cid=6487637|issn=0269-8889|access-date=2021-09-04|archive-date=2023-07-02|archive-url=https://web.archive.org/web/20230702140030/https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/data-mining-past-present-and-future/EE2E494D98BCE76EBE3FE07897540C43|url-status=live}}</ref> The proliferation, ubiquity and increasing power of computer technology have dramatically increased data collection, storage, and manipulation ability. As [[data set]]s have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, automated data processing, aided by other discoveries in computer science, specially in the field of machine learning, such as [[Artificial neural network|neural networks]], [[cluster analysis]], [[genetic algorithms]] (1950s), [[decision tree learning|decision trees]] and [[decision rules]] (1960s), and [[support vector machines]] (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns.<ref name="Kantardzic">{{cite book |last=Kantardzic |first=Mehmed |title=Data Mining: Concepts, Models, Methods, and Algorithms |year=2003 |publisher=John Wiley & Sons |isbn=978-0-471-22852-3 |oclc=50055336 |url-access=registration |url=https://archive.org/details/dataminingconcep0000kant }}</ref> in large data sets. It bridges the gap from [[applied statistics]] and artificial intelligence (which usually provide the mathematical background) to [[database management]] by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to ever-larger data sets.