Relaxation de contraintes pour l'extraction de motifs

Propone una solución al problema de la flexibilización de restricciones para la extracción de patrones. La solución propuesta, basada en el uso de la conjunción disyuntiva, parece a la vista de las pruebas efectuadas llevar a una extracción completa de los patrones equivalentes al conjunto de soluciones, por lo tanto no requiere post - procesado. La complejidad de los cálculos es casi insignificante, por lo que la implementación de éste método es perfectamente factible, para el presente trabajo se ha propuesto la flexibilización de dos restricciones n-arias muy conocida (i.e. reglas de excepción, reglas inesperadas). Sin embargo, en aplicaciones reales, existen otros tipos de restricciones n-arias que necesitan ser tomadas en cuenta. Este marco de flexibilización debe ser extendido para otros casos de estudio como el clustering (i.e. agrupamiento) donde flexibilizar restricciones es a menudo necesaria para encontrar soluciones.Tesi

Integrating constraint programming and itemset mining

Over the years many pattern mining tasks and algorithms have been proposed. Traditionally, the focus of these studies was on the efficiency of the computation and the scalability towards very large databases. Little research has however been done on a general framework that encompasses several of these problems. In earlier work we showed how constraint programming (CP) can offer such a general framework; unfortunately, however, we also found that out-of-the-box CP solvers lack the efficiency and scalability achieved by specialized itemset mining systems, which could discourage their use. Here we study the question whether a framework can be built that inherits the generality of CP systems and the efficiency of specialized algorithms. We propose a CP-based framework for pattern mining that avoids the redundant representations and propagations found in existing CP systems. We show experimentally that an implementation of this framework performs comparable to specialized itemset mining systems; furthermore, under certain conditions it lists itemsets with polynomial delay, which demonstrates that it also is a promising approach for analyzing pattern mining tasks from more theoretical perspectives. This is illustrated on a graph mining problem.acceptance rate = 19.0%status: publishe

Declarative Pattern Mining using Constraint Programming (Een declaratieve aanpak tot pattern mining door middel van constraint programming)

The goal of pattern mining is to discover patterns in data. Many techniques have been proposed for this task, differing in the type of patterns they find. To ensure that only patterns of interest are found, a common approach is to impose constraints on the patterns. Constraint-based mining systems exist in which multiple constraints can be specified. However, combining constraints in new ways or adding complex constraints requires changing the underlying algorithms. A truly general approach to constraint-based pattern mining has been missing.In this thesis we propose a general, declarative approach to pattern mining based on constraint programming. In a declarative approach one specifies what patterns need to be found, instead of algorithmically specifying how they must be found. Constraint programming offers a methodology in which a problem is stated in terms of constraints and a generic solver finds the solutions.A first contribution of this thesis is that we show how constraint programming can be used to solve constraint-based, closed and discriminative itemset mining problems as well as combinations thereof. A second contribution is that we demonstrate how the difference in performance between general constraint solvers and specialised mining algorithms can be reduced. A third contribution is the introduction of the k-pattern set mining problem, which involves finding a set of k patterns that together satisfy constraints. We propose a high-level declarative language for k-pattern set mining as well as a transformation of this language to constraint programming. Finally we apply our declarative pattern mining framework on a challenging problem in bioinformatics, namely cis-regulatory module detection. For this application, the ability to add domain-specific constraints and to combine them with existing constraints is essential.Hence we investigate for the first time how constraint programming can be used in pattern mining. We conclude on this promising approach with several remaining challenges.1 Introduction 1.1 Data Mining 1.2 Constraint Programming 1.3 Contributions 1.4 Structure of the thesis 2 Background 2.1 Pattern Mining 2.2 Constraint Programming (CP) 3 Constraint-based Itemset Mining using CP 3.1 Introduction 3.2 Frequent Itemset Mining 3.3 Closed Itemset Mining 3.4 Discriminative Itemset Mining 3.5 Itemset Mining with Costs 3.6 Experiments 3.7 Conclusions 4 Integrating Constraint Programming and Itemset Mining 4.1 Introduction 4.2 Existing Methods 4.3 Comparison of Methods 4.4 An Integrated Approach 4.5 Complexity Analysis 4.6 Experiments 4.7 Conclusions 5 k-Pattern Set Mining using CP 5.1 Introduction 5.2 k-Pattern Set Mining 5.3 Constraint Programming Model 5.4 Experiments 5.5 Related Work 5.6 Conclusions 6 Evaluating Pattern Set Mining Strategies using CP 6.1 Introduction 6.2 Pattern Set Mining Task 6.3 Constraint Programming Model 6.4 Experiments 6.5 Conclusions 7 cis-Regulatory Module Detection using CP 7.1 Introduction 7.2 Method Overview 7.3 Constraint Programming Model 7.4 Experiments 7.5 Conclusions 8 Conclusions and Future Work 8.1 Summary and conclusions 8.2 Discussion and Future worknrpages: 220status: publishe