摘要：統(tǒng)計學(xué)和數(shù)據(jù)挖掘有很多共同點，但與此同時它們也有很多差異。本文討論了兩門學(xué)科的性質(zhì)，重點論述它們的異同。
關(guān)鍵詞：統(tǒng)計學(xué) 知識發(fā)現(xiàn)
1． 簡介
統(tǒng)計學(xué)和數(shù)據(jù)挖掘有著共同的目標(biāo)：發(fā)現(xiàn)數(shù)據(jù)中的結(jié)構(gòu)。事實上，由于它們的目標(biāo)相似，一些人（尤其是統(tǒng)計學(xué)家）認(rèn)為數(shù)據(jù)挖掘是統(tǒng)計學(xué)的分支。這是一個不切合實際的看法。因為數(shù)據(jù)挖掘還應(yīng)用了其它領(lǐng)域的思想、工具和方法，尤其是計算機學(xué)科，例如數(shù)據(jù)庫技術(shù)和機器學(xué)習(xí)，而且它所關(guān)注的某些領(lǐng)域和統(tǒng)計學(xué)家所關(guān)注的有很大不同。
統(tǒng)計學(xué)和數(shù)據(jù)挖掘研究目標(biāo)的重迭自然導(dǎo)致了迷惑。事實上，有時候

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