Characterization of Interestingness Measures Using Correlation Analysis and Association Rule Mining

Abstract

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.

Discovery of associated consumer demands: Construction of a co-demanded product network with community detection

“…This method's efficiency is limited in practice because it usually prunes only a few redundant rules (Raeder & Chawla, 2011). Another solution is to filter the interesting rules from the candidate association rules based on either objective or subjective interestingness measures (Gan et al, 2018;Sethi & Shekar, 2019;Somyanonthanakul & Theeramunkong, 2020). However, such measures rank association rules ambiguously and differently, and many of them are considered a "deviation from independence"; that is, an association rule may be ranked high by one interestingness measure but ranked low by another (Raeder & Chawla, 2011).…”