标题：An Efficient Framework for Extracting Significant Associations among GO_Terms in Breast Cancer Dataset Using Association Rule Mining by Apriori Algorithm
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2015
卷号：6
期号：4
页码：215-221
语种：English
出版社：Ayushmaan Technologies
摘要：This research paper focuses on data mining in Bioinformatics, particularly in which Association Rule Mining is explored. The objective of the proposed research work is to identify the significant associations present among GO_Terms in genes in the breast cancer dataset by applying apriori algorithm for two matrices. The two matrices namely binary encoded matrix and weight encoded matrix are constructed considering direct GO_Terms and indirectGO_Terms which includes level 1 and level 2 GOs for direct GO_Terms appears in genes in the experimental dataset. Finally, the association rules identified from apriori algorithm for two matrices are compared. It is observed that, the apriori applied for weight encoded matrix identifies more number of significant rules and also hidden associations which are not identified by the apriori applied for binary encoded matrix. The research work has been implemented in Matlab tool and .NET framework.
