Handling very large numbers of association rules in the analysis of microarray data

The problem of analyzing microarray data became one of important topics in bioinformatics over the past several years, and different data mining techniques have been proposed for the analysis of such data. In this paper, we propose to use association rule discovery methods for determining associations among expression levels of different genes. One of the main problems related to the discovery of these associations is the scalability issue. Microarrays usually contain very large numbers of genes that are sometimes measured in 10,000s. Therefore, analysis of such data can generate a very large number of associations that can often be measured in millions. The paper addresses this problem by presenting a method that enables biologists to evaluate these very large numbers of discovered association rules during the post-analysis stage of the data mining process. This is achieved by providing several rule evaluation operators, including rule grouping, filtering, browsing, and data inspecti...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Alexander Tuzhilin, Gediminas Adomavicius
• Keywords: Data Inspection Operators, Data Mining, KDD 2002, Microarray Data, Rule Evaluation Operators