355 research outputs found

    Data mining by means of generalized patterns

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    The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

    Expressive generalized itemsets

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    Generalized itemset mining is a powerful tool to discover multiple-level correlations among the analyzed data. A taxonomy is used to aggregate data items into higher-level concepts and to discover frequent recurrences among data items at different granularity levels. However, since traditional high-level itemsets may also represent the knowledge covered by their lower-level frequent descendant itemsets, the expressiveness of high-level itemsets can be rather limited. To overcome this issue, this article proposes two novel itemset types, called Expressive Generalized Itemset (EGI) and Maximal Expressive Generalized Itemset (Max-EGI), in which the frequency of occurrence of a high-level itemset is evaluated only on the portion of data not yet covered by any of its frequent descendants. Specifically, EGI s represent, at a high level of abstraction, the knowledge associated with sets of infrequent itemsets, while Max-EGIs compactly represent all the infrequent descendants of a generalized itemset. Furthermore, we also propose an algorithm to discover Max-EGIs at the top of the traditionally mined itemsets. Experiments, performed on both real and synthetic datasets, demonstrate the effectiveness, efficiency, and scalability of the proposed approac

    Pattern Set Mining with Schema-based Constraint

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    Pattern set mining entails discovering groups of frequent itemsets that represent potentially relevant knowledge. Global constraints are commonly enforced to focus the analysis on most interesting pattern sets. However, these constraints evaluate and select each pattern set individually based on its itemset characteristics. This paper extends traditional global constraints by proposing a novel constraint, called schema-based constraint, tailored to relational data. When coping with relational data itemsets consist of sets of items belonging to distinct data attributes, which constitute the itemset schema. The schema-based constraint allows us to effectively combine all the itemsets that are semantically correlated with each other into a unique pattern set, while filtering out those pattern sets covering a mixture of different data facets or giving a partial view of a single facet. Specifically, it selects all the pattern sets that are (i) composed only of frequent itemsets with the same schema and (ii) characterized by maximal size among those corresponding to that schema. Since existing approaches are unable to select one representative pattern set per schema in a single extraction, we propose a new Apriori-based algorithm to efficiently mine pattern sets satisfying the schema-based constraint. The experimental results achieved on both real and synthetic datasets demonstrate the efficiency and effectiveness of our approach
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