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{{Machine learning bar}}

 

数据挖掘是在大型数据集中发现模式的过程，是一种涉及到机器学习、统计学和数据库系统综合使用的方法。<ref name="acm">{{cite web |url=http://www.kdd.org/curriculum/index.html |title=Data Mining Curriculum |publisher=[[Association for Computing Machinery|ACM]] [[SIGKDD]] |date=2006-04-30 |accessdate=2014-01-27 }}</ref><ref name="brittanica">{{cite web |last=Clifton |first=Christopher |title=Encyclopædia Britannica: Definition of Data Mining |year=2010 |url=http://www.britannica.com/EBchecked/topic/1056150/data-mining |accessdate=2010-12-09 }}</ref><ref name="elements">{{cite web|last1=Hastie|first1=Trevor|authorlink1=Trevor Hastie|last2=Tibshirani|first2=Robert|authorlink2=Robert Tibshirani|last3=Friedman|first3=Jerome|authorlink3=Jerome H. Friedman|title=The Elements of Statistical Learning: Data Mining, Inference, and Prediction|year=2009|url=http://www-stat.stanford.edu/~tibs/ElemStatLearn/|accessdate=2012-08-07|archive-url=https://web.archive.org/web/20091110212529/http://www-stat.stanford.edu/~tibs/ElemStatLearn/|archive-date=2009-11-10|url-status=dead}}</ref><ref>{{cite book|last1=Han, Kamber, Pei|first1=Jaiwei, Micheline, Jian|title=Data Mining: Concepts and Techniques|date=June 9, 2011|publisher=Morgan Kaufmann|isbn=978-0-12-381479-1|edition=3rd}}</ref>数据挖掘是指“在数据库中知识发现KDD”过程中的分析步骤。除了传统的分析步骤，它还涉及数据库和数据管理方面，包括数据预处理、模型和推理考虑、'''兴趣权值考量'''、复杂性考量、发现结构的后处理、可视化和在线更新等内容。

 

'''Data mining''' is the process of discovering patterns in large [[data set]]s involving methods at the intersection of [[machine learning]], [[statistics]], and [[database system]]s.<ref name="acm" /> Data 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.<ref name="acm">{{cite web |url=http://www.kdd.org/curriculum/index.html |title=Data Mining Curriculum |publisher=[[Association for Computing Machinery|ACM]] [[SIGKDD]] |date=2006-04-30 |accessdate=2014-01-27 }}</ref><ref name="brittanica">{{cite web |last=Clifton |first=Christopher |title=Encyclopædia Britannica: Definition of Data Mining |year=2010 |url=http://www.britannica.com/EBchecked/topic/1056150/data-mining |accessdate=2010-12-09 }}</ref><ref name="elements">{{cite web|last1=Hastie|first1=Trevor|authorlink1=Trevor Hastie|last2=Tibshirani|first2=Robert|authorlink2=Robert Tibshirani|last3=Friedman|first3=Jerome|authorlink3=Jerome H. Friedman|title=The Elements of Statistical Learning: Data Mining, Inference, and Prediction|year=2009|url=http://www-stat.stanford.edu/~tibs/ElemStatLearn/|accessdate=2012-08-07|archive-url=https://web.archive.org/web/20091110212529/http://www-stat.stanford.edu/~tibs/ElemStatLearn/|archive-date=2009-11-10|url-status=dead}}</ref><ref>{{cite book|last1=Han, Kamber, Pei|first1=Jaiwei, Micheline, Jian|title=Data Mining: Concepts and Techniques|date=June 9, 2011|publisher=Morgan Kaufmann|isbn=978-0-12-381479-1|edition=3rd}}</ref> Data mining is the analysis step of the "knowledge discovery in databases" process or KDD.<ref name="Fayyad" /> Aside from the raw analysis step, it also involves database and [[data management]] aspects, [[data pre-processing]], [[statistical model|model]] and [[Statistical inference|inference]] considerations, interestingness metrics, [[Computational complexity theory|complexity]] considerations, post-processing of discovered structures, [[Data visualization|visualization]], and [[Online algorithm|online updating]].<ref name="acm" />

 

 

'''<font color="#ff8000">数据挖掘 Data Mining </font>'''是在大型数据集中发现模式的过程，是一种涉及到机器学习、统计学和数据库系统综合使用的方法。数据挖掘是指“在数据库中知识发现KDD”过程中的分析步骤。除了传统的分析步骤，它还涉及数据库和数据管理方面，包括数据预处理、模型和推理考虑、'''兴趣权值考量'''、复杂性考量、发现结构的后处理、可视化和在线更新等内容。

   --[[用户:Zengsihang|Zengsihang]]（[[用户讨论:Zengsihang|讨论]]） 【审校】“数据挖掘是指“数据库中的知识发现KDD”的过程的分析步骤”一句中的“在数据库中知识发现KDD”处改为“知识发现(knowledge discovery in databases,KDD)”

   --[[用户:Zengsihang|Zengsihang]]（[[用户讨论:Zengsihang|讨论]]） 【审校】“数据挖掘是指“数据库中的知识发现KDD”的过程的分析步骤”一句中的“在数据库中知识发现KDD”处改为“知识发现(knowledge discovery in databases,KDD)”