Open AccessComparison Between RISS and DCHARM for Mining Gene Expression Data
Author(s) -
Shaymaa S Mousa
Publication year - 2013
Publication title -
international journal of data mining and knowledge management process
Language(s) - English
Resource type - Journals
eISSN - 2231-007X
pISSN - 2230-9608
DOI - 10.5121/ijdkp.2013.3505
Subject(s) - computer science , expression (computer science) , data mining , programming language
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 outper forms dCHARM in terms of processing time