期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2013
卷号：3
期号：5
DOI：10.5121/ijdkp.2013.3505
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Since the rapid advance of microarray technology, gene expression data are gaining recent interest to reveal biological information about genes functions and their relation to health. Data mining techniques are effective and efficient in extracting useful patterns. Most of the current data mining algorithms suffer from high processing time while generating frequent itemsets. The aim of this paper is to provide a comparative study of two Closed Frequent Itemsets algorithms (CFI), dCHARM and RISS. They are examined with high dimension data specifically gene expression data. Nine experiments are conducted with different number of genes to examine the performance of both algorithms. It is found that RISS outperforms dCHARM in terms of processing time..
