Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Hierarchical Clustering of Symbolic Objects based on Asymmetric Proximity

비대칭적 유사도 기반의 심볼릭 객체의 계층적 클러스터링

  • 오승준 (경기과학기술대학교 산업경영과) ;
  • 박찬웅 (가천대학교 산업경영공학과)
  • Received : 2012.10.05
  • Accepted : 2012.12.07
  • Published : 2012.12.25
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Abstract

Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.

패턴 인식, 데이터 분석, 침입 탐지, 이미지 처리, 바이오 인포매틱스 등과 같은 수많은 분야에서 클러스터링 분석이 사용되고 있다. 기존의 많은 연구들은 수치 데이터에만 기반을 두고 있다. 그러나 구간 데이터, 히스토그램, 심지어는 함수들을 값으로 갖는 변수들을 다루는 심볼릭 데이터 분석이 부상하고 있다. 본 논문에서는 이런 심볼릭 데이터들을 클러스터링하기 위하여 비대칭적 유사도를 제안한다. 또한 평균 유사도 값(ASV)에 기반한 클러스터링 방법도 개발한다. 제안하는 클러스터링의 결과는 기존 방법들과 다르며, 매우 고무적인 결과를 보여준다.

Keywords

References

  1. J. Han and M. Kamber, Data mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001.
  2. S. Asharaf, M. N. Murty, S. K. Shevade, "Rough set based incremental clustering of interval data," Pattern Recognition Letters, vol. 27, pp. 515-519, 2006. https://doi.org/10.1016/j.patrec.2005.09.018
  3. T. V. Ravi and K. C. Gowda, "A new nonhierarchical clustering procedure for symbolic objects," LNCS 1983, pp. 35-41, 2000.
  4. T. Denoeux and M. Masson, "Multidimensional scaling of interval valued dissimilarity data," Pattern Recognition Letters, vol. 21, pp. 83-92, 2000. https://doi.org/10.1016/S0167-8655(99)00135-X
  5. K. C. Gowda and E. Diday, "Symbolic clustering using a new dissimilarity measure," Pattern Recognition, vol. 24, no. 6, pp. 567-578, 1991. https://doi.org/10.1016/0031-3203(91)90022-W
  6. K. C. Gowda and E. Diday, "Symbolic clustering using a new similarity measure," IEEE Trans. SMC, vol. 22, no. 2, pp. 368-378, 1992.
  7. K. C. Gowda and T. V. Ravi, "Disivive clustering of symbolic objects using the concepts of both similarity and dissimilarity," Pattern Recognition, vol. 28, pp. 1277-1282, 1995(b). https://doi.org/10.1016/0031-3203(95)00003-I
  8. D. S. Guru, B. B. Kiranagi and P. Nagabhushan, "Multivalued type proximity measure and concept of mutual similarity value useful for clustering symbolic patterns," Pattern Recognition Letters, vol. 25, pp. 1203-1213, 2004. https://doi.org/10.1016/j.patrec.2004.03.016
  9. D. S. Guru and B. B. Kiranagi, "Multivalued type dissimilarity measure and the concept of mutual similarity value useful for clustering symbolic patterns," Pattern Recoginition Letters, vol. 25, no. 10, pp. 1203-1213, 2005.
  10. S. H. N. Prakash, Classification of remotely sensed data: some new approaches, Ph. D. Thesis, University of Mysore, Mysore, India, 1998.
  11. Y. Zho, G. Karypis, Comparison of agglomerative and partitional document clustering algorithms, Proc. 2nd SIAM Int, Conf. Data Mining, VA, 2002.
  12. M. Ichino, H. Yaguchi, "Generalized minkowski metrics for mixed feature-type data analysis," IEEE Trans. On systems, man, and cybernetics, vol. 24, 1994.
  13. K. C. Gowda and E. Diday, "Symbolic clustering using a new dissimilarity measure," Pattern Recognition, vol. 24, pp. 567-578, 1991. https://doi.org/10.1016/0031-3203(91)90022-W
  14. K. C. Gowda and T. V. Ravi, "Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity," Pattern Recognition Letters, vol. 16, pp. 647-652, 1995(a). https://doi.org/10.1016/0167-8655(95)80010-Q
  15. B. B. Kiranagi, D. S. Guru, S. Manjunath and B. S. Harish, "MNV for clustering based on non symmetric symbolic proximity," Int. J. of Recent Trends in Engineering, vol. 2, pp. 121-124, 2009.

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