By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
This publication constitutes the court cases of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
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Extra info for Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings
Example text
Naturally, the performance of these VAT-based methods is greatly dependent of the quality of the VAT images. However, while VAT has been widely used for cluster analysis, it is usually only effective at highlighting cluster tendency in data sets that contain compact well-separated clusters. Many practical applications involve data sets with highly irregular structure, which invalidate this assumption. In this paper, we propose an improved VAT (iVAT) approach to generating RDIs that combines VAT with a path-based distance transform.
2690, pp. 195–202. Springer, Heidelberg (2003) 4. : Some new indices of cluster validity. IEEE Transactions on System, Man and Cybernetics 28(3), 301–315 (1998) 5. : Estimating the number of clusters in a dataset via the gap statistics. Journal of the Royal Statistical Society. Series B, Statistical Methodology 63(2), 411–423 (2001) 6. : A dendrite method for cluster analysis. Communications in Statistics 3(1), 1–27 (1974) 7. : Indices of partition fuzziness and the detection of clusters in large sets.
4GHz CPU and 2GB memory running Windows XP. 1 Test Datasets Six synthetic data sets were used in our experiments, whose scatter plots are shown in Figure 4(a). These data sets involve irregular data structures, in which an obvious cluster centroid for each group is not necessarily available. , Iris, Vote and Multiple Features. The Face data set [26] contains 1755 images of 3 individuals, each of which was down-sampled to 30 × 40 pixels. The Gene data set [27] is a 194 × 194 matrix consisting of pairwise dissimilarities of a set of gene products from 3 protein families.