AssocExplorer: an association rule visualization system for exploratory data analysis

Authors:
Guimei Liu;Andre Suchitra;Haojun Zhang;Mengling Feng;See-Kiong Ng;Limsoon Wong
Affiliations:
National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;Institute for Infocomm Research, Singapore, Singapore;Institute for Infocomm Research, Singapore, Singapore;National University of Singapore, Singapore, Singapore
Venue:
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2012

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Abstract

We present a system called AssocExplorer to support exploratory data analysis via association rule visualization and exploration. AssocExplorer is designed by following the visual information-seeking mantra: overview first, zoom and filter, then details on demand. It effectively uses coloring to deliver information so that users can easily detect things that are interesting to them. If users find a rule interesting, they can explore related rules for further analysis, which allows users to find interesting phenomenon that are difficult to detect when rules are examined separately. Our system also allows users to compare rules and inspect rules with similar item composition but different statistics so that the key factors that contribute to the difference can be isolated.