Analysis of monotonicity properties of some rule interestingness measures

One of the crucial problems in the field of knowledge discovery is development of good interestingness measures for evaluation of the discovered patterns. In this paper, we consider quantitative, objective interestingness measures for "if..., then... " association rules. We focus on three popular interestingness measures, namely rule interest function of Piatetsky-Shapiro, gain measure of Fukuda et al., and dependency factor used by Pawlak. We verify whether they satisfy the valuable property M of monotonic dependency on the number of objects satisfying or not the premise or the conclusion of a rule, and property of hypothesis symmetry (HS). Moreover, analytically and through experiments we show an interesting relationship between those measures and two other commonly used measures of rule support and anti-support

Towards a theory unifying implicative interestingness measures and critical values consideration in MGK

The present paper shows the possibility and the benefit to compute statistical freshold for the so-called Guillaume-Kenchaff interestingness measure MGK of association rule and compares it with other measures as Confidence, Lift and Lovinger’s one. Afterwards, it proposes a theory of normalized interestingness measure unifying a set of rule quality measures in a binary context and being surprisingly centered on MGK

Measuring Interestingness – Perspectives on Anomaly Detection

We live in a data deluge. Our ability to gather, distribute, and store information has grown immensely over the past two decades. With this overabundance of data, the core knowledge discovery problem is no longer in the gathering of this data, but rather in the retrieving of relevant data efficiently. While the most common approach is to use rule interestingness to filter results of the association rule generation process, study of literature suggests that interestingness is difficult to define quantitatively and is best summarized as, “a record or pattern is interesting if it suggests a change in an established model.” In this paper we elaborate on the term interestingness, and the surrounding taxonomy of interestingness measures, anomalies, novelty and surprisingness. We review and summarize the current state of literature surrounding interestingness and associated approaches. Keywords: Interestingness, anomaly detection, rare-class mining, Interestingness measures, outliers, surprisingness, novelt

Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns

Understanding customer buying patterns is of great interest to the retail industry and has shown to benefit a wide variety of goals ranging from managing stocks to implementing loyalty programs. Association rule mining is a common technique for extracting correlations such as "people in the South of France buy ros\'e wine" or "customers who buy pat\'e also buy salted butter and sour bread." Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare the outcome of interestingness measures applied to buying patterns in the retail industry. We report on how we used CAPA to compare 34 measures applied to over 1,800 stores of Intermarch\'e, one of the largest food retailers in France

Categorization of interestingness measures for knowledge extraction

Finding interesting association rules is an important and active research field in data mining. The algorithms of the Apriori family are based on two rule extraction measures, support and confidence. Although these two measures have the virtue of being algorithmically fast, they generate a prohibitive number of rules most of which are redundant and irrelevant. It is therefore necessary to use further measures which filter uninteresting rules. Many synthesis studies were then realized on the interestingness measures according to several points of view. Different reported studies have been carried out to identify "good" properties of rule extraction measures and these properties have been assessed on 61 measures. The purpose of this paper is twofold. First to extend the number of the measures and properties to be studied, in addition to the formalization of the properties proposed in the literature. Second, in the light of this formal study, to categorize the studied measures. This paper leads then to identify categories of measures in order to help the users to efficiently select an appropriate measure by choosing one or more measure(s) during the knowledge extraction process. The properties evaluation on the 61 measures has enabled us to identify 7 classes of measures, classes that we obtained using two different clustering techniques.Comment: 34 pages, 4 figure

Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good quality measure remains a challenging task for a user. Given a quality measures evaluation matrix according to semantic properties, this paper describes how FCA can highlight quality measures with similar behavior in order to help the user during his choice. The aim of this article is the discovery of Interestingness Measures "IM" clusters, able to validate those found due to the hierarchical and partitioning clustering methods "AHC" and "k-means". Then, based on the theoretical study of sixty one interestingness measures according to nineteen properties, proposed in a recent study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure

Evaluation and optimization of frequent association rule based classification

Deriving useful and interesting rules from a data mining system is an essential and important task. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation of a large volume of rules from a database commonly occur. Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. In this paper, a systematic way to evaluate the association rules discovered from frequent itemset mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriated sequence of usage is presented. The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided. Empirical results show that with a proper combination of data mining and statistical analysis, the framework is capable of eliminating a large number of non-significant, redundant and contradictive rules while preserving relatively valuable high accuracy and coverage rules when used in the classification problem. Moreover, the results reveal the important characteristics of mining frequent itemsets, and the impact of confidence measure for the classification task