文章基本信息

标题：Mining Spatial Gene Expression Data Using Association Rules
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作者：Associate Professor M.Anandhavalli ; Dr. M.K.Ghose ; Mr. K.Gauthaman 等
期刊名称：International Journal of Computer Science and Security (IJCSS)
电子版ISSN：1985-1553
出版年度：2009
卷号：3
期号：5
页码：351-357
出版社：Computer Science Journals
摘要：One of the important problems in data mining is discovering association rules from spatial gene expression data where each transaction consists of a set of genes and probe patterns. The most time consuming operation in this association rule discovery process is the computation of the frequency of the occurrences of interesting subset of genes (called candidates) in the database of spatial gene expression data. A fast algorithm has been proposed for generating frequent itemsets without generating candidate itemsets along with strong association rules. The proposed algorithm uses Boolean vector with relational AND operation to discover frequent itemsets. Experimental results shows that combining Boolean Vector and relational AND operation results in quickly discovering of frequent itemsets and association rules as compared to general Apriori algorithm .
关键词：Association Rule; Spatial Gene expression data; Similarity Matrix; Boolean vector