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Hidden features extraction using Independent Component Analysis for improved alert clustering

Feature extraction plays an important role in reducing the computational complexity and increasing the accuracy. Independent Component Analysis (ICA) is an effective feature extraction technique for disclosing hidden factors that underlying mixed samples of random variable measurements. The computat...

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Main Authors: Alhaj, T. A., Zainal, A., Siraj, M. M.
Format: Conference or Workshop Item
Published: 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/59297/
http://dx.doi.org/10.1109/I4CT.2015.7219631
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spelling my.utm.592972021-09-26T15:29:14Z http://eprints.utm.my/id/eprint/59297/ Hidden features extraction using Independent Component Analysis for improved alert clustering Alhaj, T. A. Zainal, A. Siraj, M. M. QA75 Electronic computers. Computer science Feature extraction plays an important role in reducing the computational complexity and increasing the accuracy. Independent Component Analysis (ICA) is an effective feature extraction technique for disclosing hidden factors that underlying mixed samples of random variable measurements. The computation basic of ICA presupposes the mutual statistical independent of the non-Gaussian source signals. In this paper, we apply ICA algorithm as hidden features extraction to enhance the alert clustering performance. We tested the ICA against k- means, EM and Hierarchies unsupervised clustering algorithms to find the optimal performance of the clustering. The experimental results show that ICA effectively improves clustering accuracy. 2015 Conference or Workshop Item PeerReviewed Alhaj, T. A. and Zainal, A. and Siraj, M. M. (2015) Hidden features extraction using Independent Component Analysis for improved alert clustering. In: 2nd International Conference on Computer, Communications, and Control Technology, I4CT 2015, 21 - 23 April 2015, Kuching, Sarawak. http://dx.doi.org/10.1109/I4CT.2015.7219631
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Alhaj, T. A.
Zainal, A.
Siraj, M. M.
Hidden features extraction using Independent Component Analysis for improved alert clustering
description Feature extraction plays an important role in reducing the computational complexity and increasing the accuracy. Independent Component Analysis (ICA) is an effective feature extraction technique for disclosing hidden factors that underlying mixed samples of random variable measurements. The computation basic of ICA presupposes the mutual statistical independent of the non-Gaussian source signals. In this paper, we apply ICA algorithm as hidden features extraction to enhance the alert clustering performance. We tested the ICA against k- means, EM and Hierarchies unsupervised clustering algorithms to find the optimal performance of the clustering. The experimental results show that ICA effectively improves clustering accuracy.
format Conference or Workshop Item
author Alhaj, T. A.
Zainal, A.
Siraj, M. M.
author_facet Alhaj, T. A.
Zainal, A.
Siraj, M. M.
author_sort Alhaj, T. A.
title Hidden features extraction using Independent Component Analysis for improved alert clustering
title_short Hidden features extraction using Independent Component Analysis for improved alert clustering
title_full Hidden features extraction using Independent Component Analysis for improved alert clustering
title_fullStr Hidden features extraction using Independent Component Analysis for improved alert clustering
title_full_unstemmed Hidden features extraction using Independent Component Analysis for improved alert clustering
title_sort hidden features extraction using independent component analysis for improved alert clustering
publishDate 2015
url http://eprints.utm.my/id/eprint/59297/
http://dx.doi.org/10.1109/I4CT.2015.7219631
_version_ 1712285053730422784
score 13.211869

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