Detection of unique temporal segments by information theoretic meta-clustering

Shin Ando, Einoshin Suzuki

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

4 被引用数 (Scopus)

抄録

The central challenge in temporal data analysis is to obtain knowledge about its underlying dynamics. In this paper, we address the observation of noisy, stochastic processes and attempt to detect temporal segments that are related to inconsistencies and irregularities in its dynamics. Many conventional anomaly detection approaches detect anomalies based on the distance between patterns, and often provide only limited intuition about the generative process of the anomalies. Meanwhile, model-based approaches have difficulty in identifying a small, clustered set of anomalies. We propose Information- theoretic Meta-clustering (ITMC), a formalization of model-based clustering principled by the theory of lossy data compression. ITMC identifies a 'unique' cluster whose distribution diverges significantly from the entire dataset. Furthermore, ITMC employs a regularization term derived from the preference for high compression rate, which is critical to the precision of detection. For empirical evaluation, we apply ITMC to two temporal anomaly detection tasks. Datasets are taken from generative processes involving heterogeneous and inconsistent dynamics. A comparison to baseline methods shows that the proposed algorithm detects segments from irregular states with significantly high precision and recall.

本文言語英語
ホスト出版物のタイトルKDD '09
ホスト出版物のサブタイトルProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ページ59-67
ページ数9
DOI
出版ステータス出版済み - 11月 9 2009
イベント15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, フランス
継続期間: 6月 28 20097月 1 2009

出版物シリーズ

名前Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

その他

その他15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
国/地域フランス
CityParis
Period6/28/097/1/09

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 情報システム

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