Data mining: artificial intelligence in data analysis

Xindong Wu
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引用次数: 29

Abstract

Summary form only given. Data mining is a fast-growing area. The first Knowledge Discovery in Databases Workshop was held in August 1989, in conjunction with the 1989 International Joint Conference on Artificial Intelligence, and this workshop series became the International Conference on Knowledge Discovery and Data Mining (KDD) in 1995. In 2003, there were a total of 15 data mining conferences, most of which are listed at http://www.kdnuggets.com/meetings/meetings-2OO3-past.html. These 15 conferences do not include various artificial intelligence (AI), statistics and database conferences (and their workshops) that also solicited and accepted data mining related papers, such as DC AI, ICML, ICTAI, COMPSTAT, AI & Statistics, SIGMOD, VLDB, ICDE, and CIKM. Among various data mining conferences, KDD and ICDM (the IEEE International Conference on Data Mining) are arguably (or unarguably) the two premier ones in the field. ICDM was established in 2000, sponsored by the IEEE Computer Society, and had its first annual meeting in 2001. This work reviews the topics of interest from ICDM from an AI perspective, and analyze common topics in data mining and AI, including key AI ideas that have been used in both data mining and machine learning. We also discuss two current research projects on (1) user-centered agents for biological information exploration on the Web, and (2) dynamic classifier selection in dealing with streaming data. Both projects apply data mining techniques for intelligent analysis of large volumes of data.
数据挖掘:数据分析中的人工智能
只提供摘要形式。数据挖掘是一个快速发展的领域。第一次数据库知识发现研讨会于1989年8月举行,与1989年国际人工智能联合会议同时举行,该研讨会系列于1995年成为知识发现和数据挖掘国际会议(KDD)。2003年,总共有15个数据挖掘会议,其中大部分在http://www.kdnuggets.com/meetings/meetings-2OO3-past.html上列出。这15个会议不包括各种人工智能(AI)、统计和数据库会议(及其研讨会),如DC AI、ICML、ICTAI、COMPSTAT、AI & statistics、SIGMOD、VLDB、ICDE和CIKM,这些会议也征集和接受了与数据挖掘相关的论文。在各种数据挖掘会议中,KDD和ICDM (IEEE国际数据挖掘会议)可以说是(或无可争议的)该领域的两个主要会议。ICDM成立于2000年,由IEEE计算机协会赞助,并于2001年举行了第一届年会。这项工作从人工智能的角度回顾了ICDM中感兴趣的主题,并分析了数据挖掘和人工智能中的常见主题,包括在数据挖掘和机器学习中使用的关键人工智能思想。我们还讨论了两个当前的研究项目:(1)以用户为中心的网络生物信息探索代理,(2)处理流数据的动态分类器选择。这两个项目都应用数据挖掘技术对大量数据进行智能分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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