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A hybrid clustering algorithm based on ART2 and its application in anomaly detection

  • Yu Xin Ding*
  • , Yan Shi
  • , Yong Shi
  • , Jun Qing Jiang
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Adaptive Resonance Theory (ART) and K-means have been widely used for clustering, but those two algorithms have their own limitations. In this paper a hybrid clustering algorithm is proposed which is based on ART2 and K-Means. Firstly ATR2 is executed to find the initial cluster numbers and initial cluster centers, K-means uses these values to initialize its parameters and find new cluster centers, then these new cluster centers are sent back to ART2, ART2 use them to initialize connection weights between F1 layer and F2 layer, and get the final improved clusters. To prove its effectiveness it was applied in intrusion detection. The KDD'99 data sets are used as experimental data. Experiments show that clustering results are improved.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Pages282-286
Number of pages5
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR - Hong Kong, China
Duration: 30 Aug 200831 Aug 2008

Publication series

NameProceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Volume1

Conference

Conference2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Country/TerritoryChina
CityHong Kong
Period30/08/0831/08/08

Keywords

  • ART2
  • Anomaly detection
  • Clustering
  • K-means

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