Effective visualization is an important aspect of active data mining. In the context of association rules, this need has been driven by the large amount of rules produced from a run of the algorithm. To be able to address real user needs, the rules need to be summarized and organized so that it can be interpreted and applied in a timely manner. In this paper, we propose two visualization techniques that is an improvement over those used by existing data mining packages. In particular, we address the visualization of "differences" in the set of rules due to incremental changes in the data source. We show that visualization in this aspect is important to active data mining as it uncovers new insights not possible from inspecting individual data mining results.
Databases and Information Systems
Data Management and Analytics
International Workshop on Active Mining AM 2002, in conjunction with the IEEE International Conference on Data Mining ICDM 2002, 9-12 December
City or Country
Maebashi City, Japan
ONG, Hian-Huat; ONG, Kok-Leong; NG, Wee-Keong; and LIM, Ee Peng.
CrystalClear: Active Visualization of Association Rules. (2002). International Workshop on Active Mining AM 2002, in conjunction with the IEEE International Conference on Data Mining ICDM 2002, 9-12 December. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/902
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