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http://dx.doi.org/10.9708/jksci.2013.18.9.177

Visual Exploration based Approach for Extracting the Interesting Association Rules  

Kim, Jun-Woo (Dept. of Industrial and Management Systems Engineering, Dong-A University)
Kang, Hyun-Kyung (Division of Dental Hygiene, Silla University)
Publication Information
Journal of the Korea Society of Computer and Information / v.18, no.9, 2013 , pp. 177-187 More about this Journal
Abstract
Association rule mining is a popular data mining technique with a wide range of application domains, and aims to extract the cause-and-effect relations between the discrete items included in transaction data. However, analysts sometimes have trouble in interpreting and using the plethora of association rules extracted from a large amount of data. To address this problem, this paper aims to propose a novel approach called HTM for extracting the interesting association rules from given transaction data. The HTM approach consists of three main steps, hierarchical clustering, table-view, and mosaic plot, and each step provides the analysts with appropriate visual representation. For illustration, we applied our approach for analyzing the mass health examination data, and the result of this experiment reveals that the HTM approach help the analysts to find the interesting association rules in more effective way.
Keywords
Association rule mining; Transaction data; Pre-processing; Visualization; Hierarchical clustering;
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