Xuan-Hiep Huynh

A Graph-based Clustering Approach to Evaluate Interestingness Measures: A Tool and a Comparative Study

Finding interestingness measures to evaluate association rules has become an important knowledge quality issue in KDD. Many interestingness measures may be found in the literature, and many authors have discussed and compared interestingness properties in order to improve the choice of the most suitable measures for a given application. As interestingness depends both on the data structure and on the decision-makers goals, some measures may be relevant in some context, but not in others. Therefore, it is necessary to design new contextual approaches in order to help the decision-maker select the most suitable interestingness measures. In this paper, we present a new approach implemented by a new tool, ARQAT, for making comparisons. The approach is based on the analysis of a correlation graph presenting the clustering of objective interestingness measures and reflecting the post-processing of association rules. This graph-based clustering approach is used to compare and discuss the behavior of thirty-six interestingness measures on two prototypical and opposite datasets: a highly correlated one and a lowly correlated one. We focus on the discovery of the stable clusters obtained from the data analyzed between these thirty-six measures.

A data analysis approach for evaluating the behavior of interestingness measures

In recent years, the problem of finding the different aspects existing in a dataset has attracted many authors in the domain of knowledge quality in KDD. The discovery of knowledge in the form of association rules has become an important research. One of the most difficult issues is that an enormous number of association rules are discovered, so it is not easy to choose the best association rules or knowledge for a given dataset. Some methods are proposed for choosing the best rules with an interestingness measure or matching properties of interestingness measure for a given set of interestingness measures. In this paper, we propose a new approach to discover the clusters of interestingness measures existing in a dataset. Our approach is based on the evaluation of the distance computed between interestingness measures. We use two techniques: agglomerative hierarchical clustering (AHC) and partitioning around medoids (PAM) to help the user graphically evaluates the behavior of interestingness measures.

Evaluating interestingness measures with linear correlation graph

Making comparisons from the post-processing of association rules have become a research challenge in data mining. By evaluating interestingness value calculated from interestingness measures on association rules, a new approach based on the Pearson’s correlation coefficient is proposed to answer the question: How we can capture the stable behaviors of interestingness measures on different datasets?. In this paper, a correlation graph is used to evaluate the behavior of 36 interestingness measures on two datasets.

Extracting representative measures for the post-processing of association rules

This paper deals with finding a minimum set of interestingness measures in the stage of post-processing of asso- ciation rules. These measures, called representative measures, are calculated with the help of a medoid clustering. The main interest of this approach is to deliver a reduced set of measures that is specific and adapted to each dataset tudied. The result obtained also facilitates the validation of the best rules. Furthermore, the approach is applied to a rule-based dataset of 123228 association rules with thirty-six measures. As a result of this dataset, we obtain a reduced set of sixteen representative measures. The paper also summarizes the state-of-the-art post-processing and the relative works about interestingness measures.

A Graph-based Approach for Comparing Interestingness Measures

In the context of data mining, we use the Spearmans rank correlation coefficient in order to compare the behavior of 40 interestingness measures of association rules. Via a new graph-based approach, we can visualize not only the strong but also the weak correlations between interestingness measures. We propose to discover the stable clusters of interestingness measures (i.e. subsets of interestingness measures delivering a close rule ranking) by making comparative study on two opposite datasets (a highly correlated one and a lowly correlated one). The results show that the correlation between interestingness measures depends on data nature and rule ranks, and show also 6 stable clusters