SPOT: a system for detecting projected outliers from high-dimensional data streams
Paper
Paper/Presentation Title | SPOT: a system for detecting projected outliers from high-dimensional data streams |
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Presentation Type | Paper |
Authors | Zhang, Ji (Author), Gao, Qigang (Author) and Wang, Hai (Author) |
Journal or Proceedings Title | Proceedings of the 24th IEEE International Conference on Data Engineering (ICDE 2008) |
Number of Pages | 4 |
Year | 2008 |
Place of Publication | New York, United States |
ISBN | 9781424418374 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICDE.2008.4497638 |
Web Address (URL) of Paper | http://www.icde2008.org/ |
Conference/Event | 24th IEEE International Conference on Data Engineering (ICDE 2008) |
Event Details | 24th IEEE International Conference on Data Engineering (ICDE 2008) Event Date 07 to end of 12 Apr 2008 Event Location Cancun, Mexico |
Abstract | In this paper, we present a new technique, called Stream Projected Outlier deTector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, Sparse Subspace Template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective Genetic Algorithm (MOGA) is employed as an effective search method in unsupervised learning for finding outlying subspaces from training data. Finally, SST is able to carry out online self-evolution to cope with dynamics of data streams. This paper provides details on the motivation and technical challenges of detecting outliers from high-dimensional data streams, present an overview of SPOT, and give the plans for system demonstration of SPOT. |
Keywords | Stream Projected Outlier deTector; SPOT; outlier detection; data engineering; data streaming; high-dimensional data; multi-objective genetic algorithm; outlier detection |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
461399. Theory of computation not elsewhere classified | |
461305. Data structures and algorithms | |
Public Notes | © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Dalhousie University, Canada |
Saint Mary's University, Canada |
https://research.usq.edu.au/item/9z277/spot-a-system-for-detecting-projected-outliers-from-high-dimensional-data-streams
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