Fujiki Morii
ABSTRACT
Clustering using a difference criterion of distortion-ratios on clusters is investigated for data sets with large statistical differences of class data, where K-Means algorithm (KMA) and Learning Vector Quantization (LVQ) cannot necessarily reveal the good performance. After obtaining cluster centers by KMA or LVQ, a split and merge procedure with the difference criterion is executed. Focusing on an interesting data set which is not resolved by KMA or LVQ, some experimental clustering results based on the difference criterion and the split and merge procedure are provided.
- Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, INC., Chichester (2001). Google Scholar
Digital Library
- MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. 5th Berkeley Symp. on Math. Stat. and Prob., vol. 1, pp. 281-297. Univ. of California Press, Berkeley and Los Angeles (1967).Google Scholar
- Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988). Google ScholarDigital Library
- Gordon, A.D.: Classification, 2nd edn. Chapman and Hall, Boca Raton (1999).Google Scholar
- Linde, Y., Buzo, A., Gray, R.M.: An Algorithm for Vector Quantizer Design. IEEE Trans. Commun. 28, 84-95 (1980).Google ScholarCross Ref
- Kaukoranta, T., Franti, P., Nevalainen, O.: Iterative split-and-merge algorithm for vector quantization codebook generation. Optical Engineering 37(10), 2726-2732 (1998).Google ScholarCross Ref
- Jain, A.K.: Data Clustering: 50 Years Beyond K-Means. In: The King-Sun Fu Prize lecture delivered at the 19th ICPR (December 8, 2008).Google Scholar
- Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Berlin (1997). Google ScholarDigital Library
- Pal, N.R., Bezdek, J.C., Tsao, C.-K.: Generalized Clustering Networks and Kohonen's Self-Organizing Scheme. IEEE Trans. Neural Network 4(4), 549-557 (1993).Google ScholarDigital Library
- Morii, F., Kurahashi, K.: Clustering Based on Multiple Criteria for LVQ and KMeans Algorithm. JACIII 13(4), 360-365 (2009).Google Scholar
- Morii, F.: Clustering Based on Distortion-Ratio Criterion. In: Proc. of IEEE International Symposium on Industrial Electronics (ISIE 2009), pp. 1129-1133 (2009).Google ScholarCross Ref
- Pelleg, D., Moore, A.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Proc. of the 17th International Conf. on Machine Learning, pp. 727-734 (2000). Google ScholarDigital Library
- Ding, C., He, X.: Cluster Merging and Splitting in Hierarchical Clustering Algorithms. In: Proc. IEEE International Conference on Data Mining, pp. 139-146 (2002). Google ScholarDigital Library
Recommendations
Soft clustering criterion functions for partitional document clustering: a summary of results
CIKM '04: Proceedings of the thirteenth ACM international conference on Information and knowledge managementRecently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of inter- and intra-cluster similarity, are very effective in producing hard clustering solutions for ...
Comments