463
个编辑
更改
跳到导航
跳到搜索
第9行：
第9行： − 数据挖掘是在大型数据集中发现模式的过程，涉及到机器学习、统计学和数据库系统的交叉方法。数据挖掘是“数据库中的知识发现”过程的分析步骤。除了原始的分析步骤，它还涉及数据库和数据管理方面，数据预处理，模型和推理考虑，兴趣度量，复杂性考虑，发现结构的后处理，可视化和在线更新。+ 第17行：
第17行： − “数据挖掘”这个术语用词不当，因为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)。它也是一个流行词，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) ，以及计算机决策支持系统的任何应用，包括人工智能(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(主要包括机器学习材料)最初被命名为实用机器学习，而数据挖掘这个术语只是出于市场营销的原因而添加的。通常更一般的术语(大规模)数据分析和分析——或者，当涉及到实际的方法时，人工智能和机器学习——更合适。+
无编辑摘要
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 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 mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 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.
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。数据挖掘是指“数据库中的知识发现 KDD”的过程的分析步骤。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、兴趣度量、复杂性考虑、发现结构的后处理、可视化和在线更新等内容。
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)。它也是一个流行词，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) ，以及计算机决策支持系统的任何应用，包括人工智能(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(主要包括机器学习材料)最初被命名为实用机器学习，而数据挖掘这个术语只是出于市场营销的原因而添加的。通常更一般的术语(大规模)数据分析和分析——或者，当涉及到实际的方法时，人工智能和机器学习——更合适。