IEICE TRANSACTIONS on Information
Fuzzy Multiple Subspace Fitting for Anomaly Detection
Summary :
Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.10 pp.2730-2738
- Publication Date
- 2014/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7027
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Raissa RELATOR
Gunma University
Tsuyoshi KATO
Gunma University
Takuma TOMARU
Gunma University
Naoya OHTA
Gunma University
Keyword
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Raissa RELATOR, Tsuyoshi KATO, Takuma TOMARU, Naoya OHTA, "Fuzzy Multiple Subspace Fitting for Anomaly Detection" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 10, pp. 2730-2738, October 2014, doi: 10.1587/transinf.2014EDP7027.
Abstract: Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7027/_p
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@ARTICLE{e97-d_10_2730,
author={Raissa RELATOR, Tsuyoshi KATO, Takuma TOMARU, Naoya OHTA, },
journal={IEICE TRANSACTIONS on Information},
title={Fuzzy Multiple Subspace Fitting for Anomaly Detection},
year={2014},
volume={E97-D},
number={10},
pages={2730-2738},
abstract={Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.},
keywords={},
doi={10.1587/transinf.2014EDP7027},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Fuzzy Multiple Subspace Fitting for Anomaly Detection
T2 - IEICE TRANSACTIONS on Information
SP - 2730
EP - 2738
AU - Raissa RELATOR
AU - Tsuyoshi KATO
AU - Takuma TOMARU
AU - Naoya OHTA
PY - 2014
DO - 10.1587/transinf.2014EDP7027
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2014
AB - Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.
ER -
English
