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Clustering Local Frequency Items in Multiple Data Sources

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Book cover Data Analysis and Pattern Recognition in Multiple Databases

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 61))

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Abstract

Frequent items could be considered as a generic type of pattern in a database. In the context of multiple data sources most of the global patterns are based on local frequency items. A multi-branch company transacting from different branches often needs to extract global patterns from data distributed over the branches. Global decisions could be made effectively using such patterns. Thus it becomes important to cluster local frequency items in multiple databases. In this chapter an overview of the existing measures of association is presented. For the purpose of selecting the suitable technique of mining multiple databases we have surveyed the existing multi-database mining techniques. A study on the related clustering techniques is also covered here. We present the notion of high frequency itemsets, and an algorithm for synthesizing the supports of such itemsets is designed. The existing clustering technique might cluster a set of items at a low level since it estimates association among items in an itemset with low accuracy, and thus a new algorithm for clustering local frequency items is proposed. Due to the suitability of measure of association A 2, on its basis, association among items in a high frequency itemset is synthesized. The soundness of the clustering technique has been shown. Numerous experiments are conducted using five datasets, and the results concerning different aspects of the proposed problem are presented in the experimental section. The effectiveness of the proposed clustering technique is more visible in dense databases.

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Authors and Affiliations

  1. Parvatibai Chowgule College, P. O. Fatorda, Margao, Goa, 403 602, India

    Animesh Adhikari

  2. Narayan Zantye College, P. O. Bicholim Industrial Estate, Bicholim, 403 529, India

    Jhimli Adhikari

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, ABT6G2V4, Canada

    Witold Pedrycz

Authors
  1. Animesh Adhikari
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  2. Jhimli Adhikari
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  3. Witold Pedrycz
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Correspondence to Animesh Adhikari .

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Adhikari, A., Adhikari, J., Pedrycz, W. (2014). Clustering Local Frequency Items in Multiple Data Sources. In: Data Analysis and Pattern Recognition in Multiple Databases. Intelligent Systems Reference Library, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-03410-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-03410-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03409-6

  • Online ISBN: 978-3-319-03410-2

  • eBook Packages: EngineeringEngineering (R0)

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