学术
遗传进化理论及其在数据挖掘中的应用
万旺根[1] 崔滨[1] Erik D．Goodman[2]
[1]上海大学通信与信息工程学院,上海200072 [2]美国密歇根州立大学电子与计算机工作工程系,兰辛USA48824
摘 要:
遗传进化理论由美国密歇根大学J．Holland教授提出，该理论借鉴生物遗传机制，以群体方法进行自适应搜索，受到广泛关注，并在科学研究中得到广泛应用。数据挖掘从大量数据中提取信息与知识，遗传算法具有群体搜索策略和简单的遗传算子，可以实现整个数据空间上的分布式信息搜索和采集，在数据挖掘领域得到广泛应用。本文综述了遗传算法的起源、基本原理和特点，介绍了数据挖掘的应用和发展，阐述了近年来遗传算法在分类规则挖掘和关联规则挖掘方面的应用。最后对遗传算法在数据挖掘中的应用前景和面临的挑战进行了分析和展望。[著者文摘]
文章出处:
《自然杂志》-2008年30卷1期 -39-43页
栏目信息:
The Theory of Genetic Algorithms and Its Applications on Data Mining
WAN Wang-gen, CUI Bin, Erik D. Goodman（1 Professor,2 Ph . D. , School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China ; 3 Professor, Department of Electrical and Computer Engineering, Michigan State University, Lansing 48824, U.S. A.）
Abstract:
The theory of genetic algorithms was established by Professor J. Holland of the University of Michigan who was inspired by biological gene mechanism. The theory of genetic algorithms which takes advantage of group adaptive search method has received broad attention and is widely used in scientific research, Meanwhile, data mining is also widely used to obtain information and knowledge from volume data, With a simple group search strategy and a genetic operator, genetic algorithms can realize distributed searching and information gathering function from a whole data space, Therefore, the theory of genetic algorithms is widely used in the field of data mining, In this paper, the applications of the genetic evolution were discussed on the classification rules mining and the association rules mining. Besides, the origin, basic principles and features of the genctic algorithm were reviewed. The development of data mining and its applications were also discussed. Finally, the prospects and challenges of the application of the genctic algorithms on data mining were forecasted.[著者文摘]
Key words:
genetic algorithm; data mining;classification rules mining;association rules mining
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