In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application. Researchers in this field mainly focus on three main directions: clustering based on the properties of the measures, clustering based on the behavior of measures and clustering tendency of variation in statistical implications. In this paper we propose a new approach to cluster the objective interestingness measures based on tendency of variation in statistical implications. In this proposal, we built the statistical implication data of 31 objective interestingness measures based on the examination of the partial derivatives on four parameters. From this data, two distance matrices of interestingness measures are established based on Euclidean and Manhattan distance. The similarity trees are built based on distance matrix that gave results of 31 measures clustering with two different clustering thresholds.
The creation of the interestingness measures for evaluating the quality of the association rule-based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).
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