A novel method for detecting outlying subspaces in high-dimensional databases using genetic algorithm
Paper
Paper/Presentation Title | A novel method for detecting outlying subspaces in high-dimensional databases using genetic algorithm |
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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 6th IEEE International Conference on Data Mining (ICDM 2006) |
Number of Pages | 10 |
Year | 2006 |
Place of Publication | New York, United States |
ISBN | 0769527019 |
Web Address (URL) of Paper | http://www.comp.hkbu.edu.hk/~wii06/icdm/ |
Conference/Event | 6th IEEE International Conference on Data Mining (ICDM 2006) |
Event Details | 6th IEEE International Conference on Data Mining (ICDM 2006) Event Date 18 to end of 20 Dec 2006 Event Location Hong Kong, China |
Abstract | [Abstract]: Detecting outlying subspaces is a relatively new research problem in outlier-ness analysis for high-dimensional data. An outlying subspace for a given data point p is the subspace in which p is an outlier. Outlying subspace detection can facilitate a better characterization process for the detected outliers. It can also enable outlier mining for high-dimensional data to be performed more accurately and efficiently. In this paper, we proposed a new method using genetic algorithm paradigm for searching outlying subspaces efficiently. We developed a technique for efficiently computing the lower and upper bounds of the distance between a given point and its kth nearest neighbor in each possible subspace. These bounds are used to speed up the fitness evaluation of the designed genetic algorithm for outlying subspace detection. We also proposed a random sampling technique to further reduce the computation of the genetic algorithm. The optimal number of sampling data is specified to ensure the accuracy of the result. We show that the proposed method is efficient and effective in handling outlying subspace detection problem by a set of experiments conducted on both synthetic and real-life datasets. |
Keywords | outlying subspaces |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © 2006 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/9z27x/a-novel-method-for-detecting-outlying-subspaces-in-high-dimensional-databases-using-genetic-algorithm
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