29 research outputs found

    Parallel Mining of Association Rules Using a Lattice Based Approach

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    The discovery of interesting patterns from database transactions is one of the major problems in knowledge discovery in database. One such interesting pattern is the association rules extracted from these transactions. Parallel algorithms are required for the mining of association rules due to the very large databases used to store the transactions. In this paper we present a parallel algorithm for the mining of association rules. We implemented a parallel algorithm that used a lattice approach for mining association rules. The Dynamic Distributed Rule Mining (DDRM) is a lattice-based algorithm that partitions the lattice into sublattices to be assigned to processors for processing and identification of frequent itemsets. Experimental results show that DDRM utilizes the processors efficiently and performed better than the prefix-based and partition algorithms that use a static approach to assign classes to the processors. The DDRM algorithm scales well and shows good speedup

    Algorithme distribué pour l'extraction des fréquents maximaux

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    International audienceL'extraction des ensembles fréquents maximaux est un problÚme clef en fouille de données. Nous présentons dans cet article un algorithme distribué qui réalise cette tùche. Il s'agit du premier algorithme distribué avec des garanties de performance prouvées théoriquement

    Contributions Ă  l’Optimisation de RequĂȘtes Multidimensionnelles

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    Analyser les donnĂ©es consiste Ă  choisir un sous-ensemble des dimensions qui les dĂ©criventafin d'en extraire des informations utiles. Or, il est rare que l'on connaisse a priori les dimensions"intĂ©ressantes". L'analyse se transforme alors en une activitĂ© exploratoire oĂč chaque passe traduit par une requĂȘte. Ainsi, il devient primordiale de proposer des solutions d'optimisationde requĂȘtes qui ont une vision globale du processus plutĂŽt que de chercher Ă  optimiser chaque requĂȘteindĂ©pendamment les unes des autres. Nous prĂ©sentons nos contributions dans le cadre de cette approcheexploratoire en nous focalisant sur trois types de requĂȘtes: (i) le calcul de bordures,(ii) les requĂȘtes dites OLAP (On Line Analytical Processing) dans les cubes de donnĂ©es et (iii) les requĂȘtesde prĂ©fĂ©rence type skyline

    Toward autonomic distributed data mining using intelligent web services.

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    This study defines a new approach for building a Web Services based infrastructure for distributed data mining applications. The proposed architecture provides a roadmap for autonomic functionality of the infrastructure hiding the complexity of implementation details and enabling the user with a new level of usability in data mining process. Web Services based infrastructure delivers all required data mining activities in a utility-like fashion enabling heterogeneous components to be incorporated in a unified manner. Moreover, this structure allows the implementation of data mining algorithms for processing data on more than one source in a distributed manner. The purpose of this study is to present a simple, but efficient methodology for determining when data distributed at several sites can be centralized and analyzed as data from the same theoretical distribution. This analysis also answers when and how the semantics of the sites is influenced by distribution in data. This hierarchical framework with advanced and core Web Services improves the current data mining capability significantly in terms of performance, scalability, efficiency, transparency of resources, and incremental extensibility

    Learning lost temporal fuzzy association rules

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    Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.EPSRC DT
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