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Graph partition and identification of cluster number in data analysis

Published: 21 February 2011 Publication History

Abstract

Modern computing with wide range of applications in different areas such as Internet, biology, and social science, involves large scale of data analysis. The relations of data can be modeled as graphs and graph partitioning problem can be effectively approximated by spectral approaches. A critically important problem in graph partition is determination of the cluster number k. Although eigengap heuristic is a principle for this problem and is supported by theory, it is difficult to be applied for the real-world data and complex graphs. In this paper, by considering the general data analysis scenario, we present an algorithm to determine the cluster number k and perform clustering task simultaneously. The experimental result shows that our algorithm works successfully even for the real world data, which is therefore a promising tool for future data analysis.

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  1. Graph partition and identification of cluster number in data analysis

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    cover image ACM Conferences
    ICUIMC '11: Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
    February 2011
    959 pages
    ISBN:9781450305716
    DOI:10.1145/1968613
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    Published: 21 February 2011

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    Author Tags

    1. graph partition
    2. spectral clustering

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    Overall Acceptance Rate 251 of 941 submissions, 27%

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