DART: a visual analytics system for understanding dynamic association rule mining

Abstract

Dynamic rule mining can discover time-dependent association rules and provide more accurate descriptions about the relationship among items at different time periods and temporal granularities. However, users still face some challenges in analyzing and choosing reliable rules from the rules identified by algorithms, because of the large number of rules, the dynamic nature of rules across different time periods and granularities and the opacity of the relationship between rules and raw data. In this paper, we present our work on the development of DART, a visual analytics system for dynamic association rule mining, to help analysts gain a better understanding of rules and algorithms. DART allows users to explore rules at different time granularities (e.g., per hour, per day, per month, etc.) and with different time periods (e.g., daily, weekly, yearly, etc.), and to examine rules at multiple levels of detail, including investigating temporal patterns of a set of rules, comparing multiple rules, and evaluating a rule with raw data. Two case studies are used to show the functions and features of DART in analyzing business data and public safety data.

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Funding

This work was supported in part by the National Natural Science Foundation of China (No. 61572344).

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Correspondence to Xiaolong Zhang.

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Zhang, H., Chen, J., Qiang, Y. et al. DART: a visual analytics system for understanding dynamic association rule mining. Vis Comput 37, 341–357 (2021). https://doi.org/10.1007/s00371-020-01803-x

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Keywords

  • Visual analytics
  • Dynamic association rule
  • Sensemaking
  • Data mining