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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. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just 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. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just 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.
“数据挖掘”这种形容其实并不十分恰当，因为我们的目标是从大量数据中提取模式和知识，而不是数据本身的提取(挖掘)。它是一个流行语，经常用于任何形式的大规模数据或信息处理（收集、提取、仓储、分析和统计）的场景下，以及'''<font color="#ff8000"> 计算机决策系统 Decision Support System，DSS</font>'''的任何应用当中，包括人工智能（例如机器学习）和商业智能。《数据挖掘：使用Java的实用机器学习工具和技术》（主要涵盖机器学习材料）一书最初被命名为“实用机器学习”，而数据挖掘一词只是为了营销的原因而增加。经常更一般的术语例如（大规模）数据分析和分析——或当提到实际的方法时使用人工智能和机器学习这样的词语更加合适。
“数据挖掘”这种形容其实并不十分恰当，因为我们的目标是从大量数据中提取模式和知识，而不是数据本身的提取(挖掘)。它是一个流行语，经常用于任何形式的大规模数据或信息处理（收集、提取、仓储、分析和统计）的场景下，以及'''<font color="#ff8000"> 计算机决策系统 Decision Support System，DSS</font>'''的任何应用当中，包括人工智能（例如机器学习）和商业智能。《数据挖掘：使用Java的实用机器学习工具和技术》（主要涵盖机器学习材料）一书最初被命名为“实用机器学习”，而数据挖掘一词只是为了营销的原因而增加。经常来说，更一般的术语如（大规模）数据分析，或实际的方法如人工智能和机器学习，是更合适的表达方式。
--[[用户:Zengsihang|Zengsihang]]（[[用户讨论:Zengsihang|讨论]]） 【审校】“经常更一般的术语例如（大规模）数据分析和分析——或当提到实际的方法时使用人工智能和机器学习这样的词语更加合适”一句改为“经常来说，更一般的术语如（大规模）数据分析，或实际的方法如人工智能和机器学习，是更合适的表达方式”
--[[用户:Zengsihang|Zengsihang]]（[[用户讨论:Zengsihang|讨论]]） 【审校】“经常更一般的术语例如（大规模）数据分析和分析——或当提到实际的方法时使用人工智能和机器学习这样的词语更加合适”一句改为“经常来说，更一般的术语如（大规模）数据分析，或实际的方法如人工智能和机器学习，是更合适的表达方式”