Two phases outlier detection in different subspaces

Zhana Bao, Wataru Kameyama

    研究成果: Conference contribution

    1 被引用数 (Scopus)

    抄録

    Mining high dimensional outliers is not fully resolved for its dimensional particularity. The existing full space based methods can find distinct outliers and neglect those hidden in some subspaces. Subspace based approaches can detect most outliers that are apparent in low dimensional spaces, while missing the invisible outliers in subspaces. This paper proposes a novel two-phase inspection model. The first phase measures neighbor's density in subspaces to find low dimensional outliers. The second phase evaluates deviation degree of neighbors in connected subspaces. The undiscovered outliers appear a fast dispersion and scatter more than its neighbors. We analysis two-phase results statistically, and merge into one score for each object. The outliers are expressed with top score objects. The evaluation on synthetic and real data sets shows that our proposal outperform state of the art algorithms in high dimensional outlier issue.

    本文言語English
    ホスト出版物のタイトルInternational Conference on Information and Knowledge Management, Proceedings
    出版社Association for Computing Machinery
    ページ57-62
    ページ数6
    2014-November
    November
    DOI
    出版ステータスPublished - 2014 11 3
    イベント7th PhD Workshop in Information and Knowledge Management, PIKM 2014, in Conjunction with the ACM CIKM 2014 Conference - Shanghai, China
    継続期間: 2014 11 3 → …

    Other

    Other7th PhD Workshop in Information and Knowledge Management, PIKM 2014, in Conjunction with the ACM CIKM 2014 Conference
    CountryChina
    CityShanghai
    Period14/11/3 → …

    ASJC Scopus subject areas

    • Business, Management and Accounting(all)
    • Decision Sciences(all)

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