DOI QR코드

DOI QR Code

군집분석 방법들을 비교하기 위한 상사그림

The Similarity Plot for Comparing Clustering Methods

  • 투고 : 2012.11.20
  • 심사 : 2013.04.11
  • 발행 : 2013.04.30

초록

군집분석을 위한 알고리즘은 매우 많다. 이러한 군집분석 방법들이 개체들을 어떻게 여러 개의 군집으로 나누는 지를 서로 비교하기 위해서는 나누어지는 군집들이 얼마나 동일한가를 알 수 있는 동의 측도가 필요하다. 우리가 고려하여야 할 군집분석 방법들이 많아질수록 덩달아 동의 측도들 값도 많아지게 된다. 그래서 복수 개의 군집분석 방법들과 대응되는 동의 측도값들을 한 눈에 확인할 수 있는 도구가 필요하다. 본 논문을 통하여 군집분석 방법들과 대응되는 동의 측도값들을 한 눈에 확인할 수 있는 그래픽도구들을 제안하고자 한다.

참고문헌

  1. Banerjee, A., Dhillon, I. S., Ghosh, J. and Sra, S. (2005). Clustering on the unit hypersphere using von Mises-Fisher distributions, Journal of Machine Learning Research, 6, 1345-1382.
  2. Brouwer, R. K. (2009). Extending the rand, adjusted rand and jaccard indices to fuzzy partitions, Journal of Intelligence and Information System, 32, 213-235. https://doi.org/10.1007/s10844-008-0054-7
  3. Campello, R. J. G. B. (2007). A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment, Pattern Recognition Letters, 28, 833-841. https://doi.org/10.1016/j.patrec.2006.11.010
  4. Ceccarelli, M. and Maratea, A. (2008). A fuzzy extension of some classical concordance measures and an efficient algorithm for their computation, KES 2008, Part III, LNAI 5179, 755-763.
  5. Fowlkes, E. and Mallows, C. (1983). A Method for comparing two hierarchical clusterings, Journal of American Statistical Association, 78, 553-569. https://doi.org/10.1080/01621459.1983.10478008
  6. Harrison, D. and Rubinfeld, D. (1978). Hedonic prices and the demand for clean air, Journal of Environmental Economics & Management, 5, 81-102. https://doi.org/10.1016/0095-0696(78)90006-2
  7. Hubert, L. and Arabie, P. (1985). Comparing partitions, Journal of Classification, 2, 193-218. https://doi.org/10.1007/BF01908075
  8. Meila, M. (2007). Comparing clustering-An information based distance, Journal of Multivariate Analysis, 98, 873-895. https://doi.org/10.1016/j.jmva.2006.11.013
  9. Monti, S., Tamayo, P., Mesirov, J. and Golub, T. (2003). Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data, Machine Learning, 52, 91-118. https://doi.org/10.1023/A:1023949509487
  10. Song, M. S. and Cho, S. S. (2004). Statistical Data Analysis Using SAS, 2nd ed., Free-Academy, Seoul.
  11. Strehl, A. and Ghosh, J. (2002). Cluster ensembles - a knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, 3, 583-617.
  12. Rand, W. (1971). Objective criteria for the evaluation of clustering methods, Journal of American Statistical Association, 66, 846-850. https://doi.org/10.1080/01621459.1971.10482356
  13. Unnikrishnan, R., Pantofaru, C. and Hebert, M. (2007). Toward objective evaluation of image segmentation Algorithms, IEEE Transactions On Pattern Analysis And Machine Intelligence, 29, 929-944. https://doi.org/10.1109/TPAMI.2007.1046
  14. Vinh, N. and Epps, J. (2009). A novel approach for automatic number of clusters detection in microarray data based on consensus clustering, 9th IEEE International Conference on Bioinformatics and Bioengineering, 84-91.
  15. Warrens, M. J. (2008). On the equivalence of Cohen's Kappa and the Hubert-Arabie adjusted Rand index, Journal of Classification, 25, 177-183. https://doi.org/10.1007/s00357-008-9023-7
  16. Yu, Z., Wong, H.-S. and Wang, H. (2007). Graph-based consensus clustering for class discovery from gene expression data, Bioinformatics, 23, 2888-2896. https://doi.org/10.1093/bioinformatics/btm463

피인용 문헌

  1. Water Quality Improvement Plan for Small Streams in the Northernmost Basin of Bukhan River based on Pollution Grade and Typological Analysis Linkage vol.32, pp.3, 2016, https://doi.org/10.15681/KSWE.2016.32.3.281