Outlier detection on mixed-type data: an energy-based approach Do, K, Tran, Truyen, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Outlier detection on mixed-type data: an energy-based approach, in ADMA 2016 : Proceedings of the 12th International Conference of Advanced Data Mining and Applications, Springer International Publishing, Cham, Switzerland, pp. 111-125, doi: 10.1007/978-3-319-49586-6_8. Attached Files Name Description MIMEType Size Downloads Title Outlier detection on mixed-type data: an energy-based approach Author(s) Do, K Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Advanced Data Mining and Applications. International Conference (12th : 2016 : Gold Coast, Queensland) Conference location Gold Coat, Queensland Conference dates 2016/12/12 - 2016/12/15 Title of proceedings ADMA 2016 : Proceedings of the 12th International Conference of Advanced Data Mining and Applications Editor(s) Li, J Li, X Wang, S Li, J Sheng, QZ Publication date 2016 Series Lecture notes in artificial intelligence Start page 111 End page 125 Total pages 15 Publisher Springer International Publishing Place of publication Cham, Switzerland Summary 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. ISBN 9783319495859 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-49586-6_8 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 0 Not Applicable HERDC Research category E1 Full written paper - refereed Copyright notice ©2016, Springer International Publishing Persistent URL http://hdl.handle.net/10536/DRO/DU:30091399 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 17 Feb 2017, 16:38:32 EST Sat, 18 Feb 2017, 04:01:16 EST Thu, 08 Jun 2017, 10:57:46 EST Thu, 08 Jun 2017, 10:59:30 EST Wed, 14 Jun 2017, 06:05:37 EST Wed, 14 Jun 2017, 15:20:24 EST Fri, 02 Feb 2018, 14:07:32 EST Fri, 02 Feb 2018, 14:08:18 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 125 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 17 Feb 2017, 16:38:32 EST

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