ART1 클러스터링 성능 향상을 위한 초기 학습순서 조정

Adjustment of initial learning order to improve clustering performance of ART1

  • 최태훈 (경희대학교 컴퓨터공학과) ;
  • 임성길 (경희대학교 컴퓨터공학과) ;
  • 이현수 (경희대학교 컴퓨터공학과)
  • Choi, Tae-Hun (Department of Computer Engineering, Kyung Hee University) ;
  • Lim, Sung-Kil (Department of Computer Engineering, Kyung Hee University) ;
  • Lee, Hyon-Soo (Department of Computer Engineering, Kyung Hee University)
  • 발행 : 2008.06.18

초록

This paper presents adjustment of input order to improve clustering performance of ART1. We propose new method for On-line clustering which adjusts initial input data using buffer. We demonstrate the clustering performance of the proposed algorithm by testing it on Zoo data set from UCI and created artificial data set for simulation. Experimental results show that preposed method increases 7.8% of clustering performance than ART1 model on the average.

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