Julien Blanchard

Using information-theoretic measures to assess association rule interestingness

Assessing rules with interestingness measures is the cornerstone of successful applications of association rule discovery. However, there exists no information-theoretic measure which is adapted to the semantics of association rules. In this article, we present the directed information ratio (DIE), a new rule interestingness measure which is based on information theory. DIR is specially designed for association rules, and in particular it differentiates two opposite rules a /spl rarr/ b and a /spl rarr/ b~. Moreover, to our knowledge, DIR is the only rule interestingness measure which rejects both independence and (what we call) equilibrium, i.e. it discards both the rules whose antecedent and consequent are negatively correlated, and the rules which have more counter-examples than examples. Experimental studies show that DIR is a very filtering measure, which is useful for association rule post-processing.

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.

Visual Analytics: A 2D-3D visualization support for human-centered rule mining

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge, the user needs to rummage through the rules. To make this task easier, we propose a new interactive mining methodology based on well adapted dynamic visual representations. It allows the user to drive the discovery process by focusing his/her attention on limited subsets of rules. We have implemented our methodology with two complementary 2D and 3D visualization supports. These implementations exploit the users focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

Domain driven data mining to improve promotional campaign ROI and select marketing channels

The trading activities of materials retail is concerned with an extremely competitive market. However, business people are not well informed about how to proceed and what to do during marketing activities. Data mining methods could be interesting to generate substantial profits for decision makers and to optimize the choice of different marketing activities. In this paper, we propose an actionable knowledge discovery methodology, for one-to-one marketing, which allows to contact the right customer through the right communication channel. This methodology first requires a measurement of the tendency for the customers to purchase a given item, and second requires an optimization of the Return On Investment by selecting the most effective communication channels for attracting these customers. Our methodology has been applied to the VM Matériaux company. Thanks to the collaboration between data miners and decision makers, we present a domain-driven view of knowledge discovery satisfying real business needs to improve the efficiency and outcome of several promotional marketing campaigns.

Visual Analysis of Topics in Twitter Based on Co-evolution of Terms

The analysis of Twitter short messages has become a key issue for companies seeking to understand consumer behaviour and expectations. However, automatic algorithms for topic tracking often extract general tendencies at a high granularity level and do not provide added value to experts who are looking for more subtle information. In this paper, we focus on the visualization of the co-evolution of terms in tweets in order to facilitate the analysis of the evolution of topics by a decision-maker. We take advantage of the perceptual quality of heatmaps to display our 3D data (term × time × score) in a 2D space. Furthermore, by computing an appropriate order to display the main terms on the heatmap, our methodology ensures an intuitive visualization of their co-evolution. An experiment was conducted on real-life datasets in collaboration with an expert in customer relationship management working at the French energy company EDF. The first results show three different kinds of co-evolution of terms: bursty features, reoccurring terms and long periods of activity.

Visualisation of Association Rules Based on a Molecular Representation

In order to extract interesting knowledge from large amounts of rules produced by the data mining algorithms, visual representations of association rules are increasingly used. These representations can help users to find and to validate interesting knowledge. All techniques proposed for visualisation of rules have been developed to represent an association rule as a whole without paying attention to the relations among the items that make up the antecedent and the consequent and the contribution of each one to the rule. In this paper, we propose a new visualisation representation for association rules that allows the visualisation of the items which make up the antecedent and the consequent, the contribution of each one to the rule, and the correlations between each pair of the antecedent and each pair of consequent.

Assessing the interestingness of temporal rules with Sequential Implication Intensity

In this article, we study the assessment of the interestingness of sequential rules (generally temporal rules). This is a crucial problem in sequence analysis since the frequent pattern mining algorithms are unsupervised and can produce huge amounts of rules. While association rule interestingness has been widely studied in the literature, there are few measures dedicated to sequential rules. Continuing with our work on the adaptation of implication intensity to sequential rules, we propose an original statistical measure for assessing sequential rule interestingness. More precisely, this measure named Sequential Implication Intensity (SII) evaluates the statistical significance of the rules in comparison with a probabilistic model. Numerical simulations show that SII has unique features for a sequential rule interestingness measure.

On the discovery of significant temporal rules

The assessment of the interestingness of sequential rules (generally temporal rules) is a crucial problem in sequence analysis. Due to their unsupervised nature, frequent pattern mining algorithms commonly generate a huge number of rules. However, while association rule interestingness has been widely studied in the literature, there are few measures dedicated to sequential rules. In this article, we propose an original statistical measure for assessing sequential rule interestingness. This measure named Sequential Implication Intensity (SII ) evaluates the statistical significance of the rules in comparison with a probabilistic model. Numerical simulations show that SII has unique features for a sequential rule interestingness measure.