Abstract
Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (Mv.RBM). The Mv.RBM is a principled probabilistic method that models data density. We propose to use free-energy derived from Mv.RBM as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of Mv.RBM is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts.
Original language | English |
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Title of host publication | Advanced Data Mining and Applications |
Subtitle of host publication | 12th International Conference, ADMA 2016 Gold Coast, QLD, Australia, December 12–15, 2016 Proceedings |
Editors | Jianxin Li, Xue Li, Shuliang Wang, Jinyan Li, Quan Z. Sheng |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 111-125 |
Number of pages | 15 |
ISBN (Electronic) | 9783319495866 |
ISBN (Print) | 9783319495859 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Conference on Advanced Data Mining and Applications 2016 - Gold Coast, Australia Duration: 12 Dec 2016 → 15 Dec 2016 Conference number: 12th https://cs.adelaide.edu.au/~adma2016/ https://link.springer.com/book/10.1007/978-3-319-49586-6 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10086 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Advanced Data Mining and Applications 2016 |
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Abbreviated title | ADMA 2016 |
Country/Territory | Australia |
City | Gold Coast |
Period | 12/12/16 → 15/12/16 |
Internet address |