Construction and evaluation of structured association map for visual exploration of association rules

作者：

Highlights：

• SAM provides enhanced visual aids for representing the many-to-many association rules in large transaction data.

• The performance of SAM can be numerically evaluated by using S2C measure.

• SAM enables users to conveniently identify the interesting areas that might contain interesting association rules.

• SAMs with higher S2C values are more useful for visual exploration of association rules.

摘要

•SAM provides enhanced visual aids for representing the many-to-many association rules in large transaction data.•The performance of SAM can be numerically evaluated by using S2C measure.•SAM enables users to conveniently identify the interesting areas that might contain interesting association rules.•SAMs with higher S2C values are more useful for visual exploration of association rules.

论文关键词：Visual exploration,Data mining,Association rule mining,Hierarchical clustering,Structured association map,Health examination

论文评审过程：Received 27 December 2015, Revised 6 January 2017, Accepted 7 January 2017, Available online 9 January 2017, Version of Record 16 January 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.007