摘要
数据挖掘方法结合了机器学习、模式识别、统计学、数据库和人工智能等众多领域的知识,是解决从大量信息中获取有用知识、提供决策支持的有效途径,具有广泛的应用前景。以关联、分类、聚类归类,对当前数据挖掘的多种方法进行了研究,并指出其现存的问题。这些方法都有局限性,多方法融合、有机组合互补将成为数据挖掘的发展趋势。
Data Mining integrates with knowledge ofnumerous fields such as machine leaming, pattemrecognition, statistics, database and artificial intelligence. It is an effective approach to fetch useful information from large database and offer decision support. There is a broad application foreground of data mining. Many latest methods range by association, classification and clustering in data mining was researched, and their remaining problems were discussed. As a whole, all these algorithms have their own limitations, and organically combining several methods will be the develooment trend for data mining.
出处
《计算机工程与设计》
CSCD
北大核心
2005年第9期2304-2307,共4页
Computer Engineering and Design
基金
国家863高技术研究发展基金项目(2002AA412020)
关键词
数据挖掘
分类算法
关联分析
分类分析
聚类分析
data mining
classification algorithm
association analysis
classification analysis
clustering analysis
