Journal - Volume I No.1 September 2014 , Special Issue-1

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  • Title

    :

    HISTORY GENERALIZED PATTERN TAXONOMY MODEL FOR FREQUENT ITEMSET MINING

    Authors

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     1 Jibin Philip  , 2K.Moorthy

    Keywords

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    Taxonomy Model, Frequent item set Mining.

    Issue Date

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    September - 2014

    Abstract

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    Frequent item set mining is a widely exploratory technique that focuses on discovering recurrent correlations among data. The steadfast evolution of markets and business environments prompts the need of data mining algorithms to discover significant correlation changes in order to reactively suit product and service provision to customer needs. Change mining, in the context of frequent item sets, focuses on detecting and reporting significant changes in the set of mined item sets from one time period to another. The discovery of frequent generalized item sets, i.e., item sets that 1) frequently occur in the source data, and 2) provide a high-level abstraction of the mined knowledge, issues new challenges in the analysis of item sets that become rare, and thus are no longer extracted, from a certain point. This paper proposes a novel kind of dynamic pattern, namely the History Generalized Pattern (HIGEN), that represents the evolution of an item set in consecutive time periods, by reporting the information about its frequent generalizations characterized by minimal redundancy (i.e., minimum level of abstraction) in case it becomes infrequent in a certain time period. To address HIGEN mining, it proposes HIGEN MINER, an algorithm that focuses on avoiding item set mining followed by post processing by exploiting a support driven item set generalization approach.

    Page(s)

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    106 – 108

    ISSN

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    2347- 4734

    Source

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    Vol. 1, No.1 - Special Issue-1

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