Interactive Outlier Detection Adaptive to Users' Intentions (夏のデータベースワークショップDBWS2004)
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概要
- 論文の詳細を見る
Detecting outliers is an important, but tricky problem, since the preference of outlier-ness often depends on the user and/or the dataset. We have developed a system of detecting outliers that match users' intentions implied by outlier examples in prior work. In this paper, we propose a new refined method of interactive outlier detection adaptive to users' intentions. Keywords Data Mining, Outlier Detection, Outlier Examples, User Interaction
- 一般社団法人情報処理学会の論文
- 2004-07-13
著者
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Faloutsos Christos
School Of Computer Science Carnegie Mellon University
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Kitagawa Hiroyuki
Graduate School Of Environmental Studies Nagoya University
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Zhu Cui
Graduate School Of Systems And Information Engineering University Of Tsukuba
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Papadimitriou Spiros
School of Computer Science, Carnegie Mellon University
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Papadimitriou Spiros
School Of Computer Science Carnegie Mellon University
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