Visual analysis of user-driven association rule mining
Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or...
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| Published in | Journal of visual languages and computing Vol. 42; pp. 76 - 85 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.10.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1045-926X 1095-8533 |
| DOI | 10.1016/j.jvlc.2017.08.007 |
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| Abstract | Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A hierarchical matrix-based visualization technique is proposed for the user to explore the measure value and the intermediate results of association rules. We also design a set of visual exploration tools to support interactively inspection and manipulation of mining process. The effectiveness and usability of our approach is demonstrated with two scenarios. |
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| AbstractList | Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A hierarchical matrix-based visualization technique is proposed for the user to explore the measure value and the intermediate results of association rules. We also design a set of visual exploration tools to support interactively inspection and manipulation of mining process. The effectiveness and usability of our approach is demonstrated with two scenarios. |
| Author | Chen, Wei Peng, Qunsheng Xie, Cong Shang, Pingping |
| Author_xml | – sequence: 1 givenname: Wei surname: Chen fullname: Chen, Wei email: chenwei@cad.zju.edu.cn organization: State Key Lab of CAD& CG Zhejiang University, Hangzhou, China – sequence: 2 givenname: Cong surname: Xie fullname: Xie, Cong email: coxie@cs.stonybrook.edu organization: Stony Brook University, New York, USA – sequence: 3 givenname: Pingping surname: Shang fullname: Shang, Pingping organization: State Key Lab of CAD& CG Zhejiang University, Hangzhou, China – sequence: 4 givenname: Qunsheng surname: Peng fullname: Peng, Qunsheng organization: State Key Lab of CAD& CG Zhejiang University, Hangzhou, China |
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| Cites_doi | 10.1109/TVCG.2014.2346913 10.4236/ijis.2013.31A005 10.1109/MCG.2017.21 10.1145/1132960.1132963 10.1179/000870403235002042 10.1109/TVCG.2016.2598479 10.1145/1010614.1010616 10.1016/j.cag.2007.01.023 10.1007/s11390-016-1663-1 |
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