A Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data-Reference-Cited by-同舟云学术

A Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data

Published:2019 Issue: Volume: Page:322-331
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Messaoud Thouraya Aouled,Smiti Abir,Louati Aymen

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-29859-3_28

Reference13 articles.

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2. Goldstein, M., Dengel, A.: Histogram-based outlier score (HBOS): a fast unsupervised anomaly detection algorithm. In: KI-2012: Poster and Demo Track, pp. 59–63 (2012)

3. Kriegel, H.-P., Zimek, A., et al.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 444–452. ACM (2008)

4. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);K Zhang,2009

5. Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: ACM SIGMOD Record, vol. 29, pp. 93–104. ACM (2000)

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