Summarizing Semantic Associations Based on Focused Association Graph

Jiang, Xiaowei; Zhang, Xiang; Gui, Weihua; Gao, Feifei; Wang, Peng; Zhou, Fengbo

doi:10.1007/978-3-642-35527-1_47

Cited by 6 publications

(1 citation statement)

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“…Association rules mining is a technique to combine data mining and semantic techniques for post-mining and selection of association rules [21]. Exponential development of semantic web, numerous linked data from social community, companies or end-users trigger new potential to connect and join data in order to share and reveal hidden relationship or "semantic association" [22]. Blanchard J. et al [18] studied in semantics-based classification of rule interestingness measures.…”

Section: Related Workmentioning

confidence: 99%

Characterization of Interestingness Measures Using Correlation Analysis and Association Rule Mining

Somyanonthanakul

Theeramunkong

2020

IEICE Trans. Inf. & Syst.

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Objective interestingness measures play a vital role in association rule mining of a large-scaled database because they are used for extracting, filtering, and ranking the patterns. In the past, several measures have been proposed but their similarities or relations are not sufficiently explored. This work investigates sixty-one objective interestingness measures on the pattern of A → B, to analyze their similarity and dissimilarity as well as their relationship. Three-probability patterns, P(A), P(B), and P(AB), are enumerated in both linear and exponential scales and each measure's values of those conditions are calculated, forming synthesis data for investigation. The behavior of each measure is explored by pairwise comparison based on these three-probability patterns. The relationship among the sixty-one interestingness measures has been characterized with correlation analysis and association rule mining. In the experiment, relationships are summarized using heat-map and association rule mined. As the result, selection of an appropriate interestingness measure can be realized using the generated heat-map and association rules.

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Section: Related Workmentioning

confidence: 99%