统计学和数据挖掘：交叉学科-----外文翻译.doc
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统计学和数据挖掘：交叉学科-----外文翻译,摘要：统计学和数据挖掘有很多共同点，但与此同时它们也有很多差异。本文讨论了两门学科的性质，重点论述它们的异同。关键词：统计学 知识发现1．简介统计学和数据挖掘有着共同的目标：发现数据中的结构。事实上，由于它们的目标相似，一些人（尤其是统计学家）认为数据挖掘是统计学的分支。这是一个不切合实际的看法。因为数据挖掘还应用了其...
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摘要：统计学和数据挖掘有很多共同点，但与此同时它们也有很多差异。本文讨论了两门学科的性质，重点论述它们的异同。
关键词：统计学 知识发现
1． 简介
统计学和数据挖掘有着共同的目标：发现数据中的结构。事实上，由于它们的目标相似，一些人（尤其是统计学家）认为数据挖掘是统计学的分支。这是一个不切合实际的看法。因为数据挖掘还应用了其它领域的思想、工具和方法，尤其是计算机学科，例如数据库技术和机器学习，而且它所关注的某些领域和统计学家所关注的有很大不同。
统计学和数据挖掘研究目标的重迭自然导致了迷惑。事实上，有时候
ABSTRACT
Statistics and data mining have much in common, but they also have differences. The nature of the two disciplines is examined,with emphasis on their similarities and differences.
Keywords
Statistics, knowledge discovery.
1. INTRODUCTION
The two disciplines of statistics and data mining have common aims in that both are concerned with discovering structure in data.Indeed, so much do their aims overlap, that some people (perhaps, in the main, some statisticians) regard data mining as a subset of statistics. This is not a realistic assessment. Data mining also makes use of ideas, tools, and methods from other areas - especially computational areas such as database technol- ogy and machine learning - and is not heavily concerned with some areas in which statist- icians are interested.
The commonality of aims between statistics and data mining has naturally caused some confusion. Indeed, it has even sometimes caused antipathy. Statistics has formal roots stretching back at least throughout this century, and the appearance of a new discip- line, with new players, who purported to be solving problems that statisticians had previ- ously considered part of their dominion, inevitably caused concern. The
关键词：统计学 知识发现
1． 简介
统计学和数据挖掘有着共同的目标：发现数据中的结构。事实上，由于它们的目标相似，一些人（尤其是统计学家）认为数据挖掘是统计学的分支。这是一个不切合实际的看法。因为数据挖掘还应用了其它领域的思想、工具和方法，尤其是计算机学科，例如数据库技术和机器学习，而且它所关注的某些领域和统计学家所关注的有很大不同。
统计学和数据挖掘研究目标的重迭自然导致了迷惑。事实上，有时候
ABSTRACT
Statistics and data mining have much in common, but they also have differences. The nature of the two disciplines is examined,with emphasis on their similarities and differences.
Keywords
Statistics, knowledge discovery.
1. INTRODUCTION
The two disciplines of statistics and data mining have common aims in that both are concerned with discovering structure in data.Indeed, so much do their aims overlap, that some people (perhaps, in the main, some statisticians) regard data mining as a subset of statistics. This is not a realistic assessment. Data mining also makes use of ideas, tools, and methods from other areas - especially computational areas such as database technol- ogy and machine learning - and is not heavily concerned with some areas in which statist- icians are interested.
The commonality of aims between statistics and data mining has naturally caused some confusion. Indeed, it has even sometimes caused antipathy. Statistics has formal roots stretching back at least throughout this century, and the appearance of a new discip- line, with new players, who purported to be solving problems that statisticians had previ- ously considered part of their dominion, inevitably caused concern. The
