Henri Briand

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.

Matching directories and OWL ontologies with AROMA

This paper presents a simple and adaptable matching method dealing with web directories, catalogs and OWL ontologies. By using a well-known Knowledge Discovery in Databases model, such as the association rule paradigm, this method has the originality to be both extensional and asymmetric. It works at the terminological level (by selecting concept-relevant terms contained in documents) and permits to discover equivalence and also subsumption relations holding between entities (concepts and properties). This method relies on the implication intensity measure, a probabilistic model of deviation from independence. Selection of significant rules between concepts (or properties) is lead by two criteria permitting to assess respectively the implication quality and the generativity of the rule. Finally, the proposed method is evaluated on two benchmarks. The first contains two conceptual hierarchies populated with textual documents and the second one is composed of OWL ontologies.

Interactive visual exploration of association rules with rule-focusing methodology

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 for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the users focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

A use case driven requirements engineering process

Regarding the requirements engineering process, approaches based on use cases seem to provide promising solutions concerning the early high-level requirements gathering problem. We propose an approach based on use cases to help the analyst during the requirements acquisition and the requirements conceptualisation activities, our final goal being to produce object-oriented specifications. The approach is “domain expert-oriented’ in the sense that domain expert(s) (in fact, one or more in our approach) can actively participate during the requirements acquisition activity by identifying and by describing the use cases. Rules are proposed during the requirements conceptualisation activity and allow the analyst to generate parts of object type state transition diagrams from a use case description.

A User-Driven Process for Mining Association Rules

This paper describes the components of a human-centered process for discovering association rules where the user is considered as a heuristic which drives the mining algorithms via a well-adapted interface. In this approach, inspired by experimental works on behaviors during a discovery stage, the rule extraction is dynamic : at each step, the user can focus on a subset of potentially interesting items and launch an algorithm for extracting the relevant associated rules according to statistical measures. The discovered rules are represented by a graph updated at each step, and the mining algorithm is an adaptation of the well-known A Priori algorithm where rules are computed locally. Experimental results on a real corpus built from marketing data illustrate the different steps of this process.

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.

Shape representation for image retrieval

akhencha I hbriand} @ireste.fr In the domain of the content-based image retrieval, the user formulates his queries from both visual and textual descriptions. In the sequel, we will only dwell on one of the most important visual features, namely the shape feature. The shape feature is essential as it corresponds to region of interest in images. Consequently, the shape representation is fundamental. This description must be compact and accurate, and it must own properties of invariance to several geometric transformations. After presenting several shape representations, we present the two complementary methods implemented in our prototype. The first one is an existing well-known approach, Freeman code, and the second one is an adaptation of a famous approach, Fourier theory. Simulations allow us to compare our results with results obtained under MATLAB, a powerful mathematical software, and to validate the proposed method.

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.

One application of neural networks for detection of defects using video data bases: identification of road distresses

Describes an application of neural networks to the discovery of road surface distresses (cracks, etc.) from video sequences. After describing the context of this application, we detail its progressive design. We initially thought of using only one neural network to totally analyze each image extracted from a video sequence. We later thought of a more simple neural network analyzing only a small part of each image each time, with a preprocessor scanning the image. We finally preferred to simplify the role of the neural network by putting an image preprocessing sequence in front, to extract objects that were then identified by the neural network. We describe the sequence of treatments that we use and detail each processing step: improvement of the original image, extraction of significant objects (possible distresses) and identification of these objects by the neural network. We conclude by evaluating the performance of our system and by discussing possible improvements.

A Tree-Based Similarity for Evaluating Concept Proximities in an Ontology

The problem of evaluating semantic similarity in a network structure knows a noticeable renewal of interest linked to the importance of the ontologies in the semantic Web. Different semantic measures have been proposed in the literature to evaluate the strength of the semantic link between two concepts or two groups of concepts within either two different ontologies or the same ontology. This paper presents a theoretical study synthesis of some semantic measures based on an ontology restricted to subsumption links. We outline some limitations of these measures and introduce a new one: the Proportion of Shared Specificity. This measure which does not depend on an external corpus, takes into account the density of links in the graph between two concepts. A numerical comparison of the different measures has been made on different large size samples from WordNet.