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[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Mining distance-based outliers from large databases in any metric space

✍ Scribed by Tao, Yufei; Xiao, Xiaokui; Zhou, Shuigeng

Book ID
120625935
Publisher
ACM Press
Year
2006
Weight
800 KB
Category
Article
ISBN-13
9781595933393

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πŸ“œ SIMILAR VOLUMES

[ACM Press the 12th ACM SIGKDD internati
[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Mining distance-based outliers from large databases in any metric space
✍ Tao, Yufei; Xiao, Xiaokui; Zhou, Shuigeng πŸ“‚ Article πŸ“… 2006 πŸ› ACM Press βš– 800 KB

Let R be a set of objects. An object o ∈ R is an outlier, if there exist less than k objects in R whose distances to o are at most r. The values of k, r, and the distance metric are provided by a user at the run time. The objective is to return all outliers with the smallest I/O cost.This paper cons

[ACM Press the 12th ACM SIGKDD internati
[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Outlier detection by active learning
✍ Abe, Naoki; Zadrozny, Bianca; Langford, John πŸ“‚ Article πŸ“… 2006 πŸ› ACM Press βš– 134 KB

Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel app