A Method of Peculiarity Factor Approximation using Vector Quantization Model
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- Saitoh Fumiaki
- Department of Industrial and Systems Engineering, Aoyama Gakuin University
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- Ishizu Syohei
- Department of Industrial and Systems Engineering, Aoyama Gakuin University
Bibliographic Information
- Other Title
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- ベクトル量子化モデルによるPeculiarity Factorの近似計算
- ベクトル リョウシカ モデル ニ ヨル Peculiarity Factor ノ キンジ ケイサン
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Abstract
The purpose of this research is to reduce the computational complexity of the Peculiarity Factor (PF). Recently, PF has been adopted as the index for anomaly data detection, and it is widely used in various mining scenes. The fact that PF has become a powerful mining tool is positive because its calculation method is extremely simple and the results of the calculation are easy to understand visually. One of the most important problems for using PF for large-scale data is the rapidly increasing computational complexity required when the data volume increases. The computational complexity of PF is in the polynomial order because the PF of each data is calculated distantly over all the data. In this study, we propose an approximation methodology for PF for computational reduction and for enhanced robustness using the vector quantization model. Approximate values of PF are calculated by replacing the actual data with the nodes of vector quantization model. By calculating PF based on the vector quantization node vectors, we achieve restraint in the increasing computational complexity.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 135 (3), 304-311, 2015
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390001204607897984
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- NII Article ID
- 130004870147
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 026245399
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed