Quantitative Association Analysis Using Tree Hierarchies

Association analysis arises in many important applications such as bioinformatics and business intelligence. Given a large collection of measurements over a set of samples, association analysis aims to find dependencies of target variables to subsets of measurements. Most previous algorithms adopt a two-stage approach; they first group samples based on the similarity in the subset of measurements, and then they examine the association between these groups and the specified target variables without considering the inter-group similarities or alternative groupings. This can lead to cases where the strength of association depends significantly on arbitrary clustering choices. In this paper, we propose a tree-based method for quantitative association analysis. Tree hierarchies derived from sample similarities represent many possible sample groupings. They also provide a natural way to incorporate domain knowledge such as ontologies and to identify and remove outliers. Given a tree hie...

• Type: Conference
• Year: 2008
• Where: ICDM
• Authors: Feng Pan, Lynda Yang, Leonard McMillan, Fernando Pardo-Manuel de Villena, David Threadgill, Wei Wang 0010
• Keywords: Association Analysis, Association Analysis Arises, Data Mining, ICDM 2008, Target Variable