By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
This e-book constitutes the lawsuits of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
Read Online or Download Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings PDF
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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
Our contributions include: 1) The VAT algorithm was enhanced by using 26 L. Wang et al. a path-based distance transform. The iVAT algorithm can better reveal the hidden cluster structure, especially for complex-shaped data sets. 2) Based on the iVAT image, the cluster structure in the data can be reliably estimated by visual inspection. As well, the aVAT algorithm was proposed for automatically determining the number of clusters c. 3) We performed a series of primary and comparative experiments on 6 synthetic data sets and 6 real-world data sets, and our methods obtained encouraging results.
3–13 (2008) 17. : Automatically determining the number of clusters in unlabeled data sets. IEEE Transactions on Knowledge and Data Engineering 21(3), 335–350 (2009) 18. : Clustering in ordered dissimilarity data. International Journal of Intelligent Systems 24(5), 504–528 (2009) 19. : Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 14, 585–591 (2002) 20. : Spectral graph theory. In: CBMS Regional Conference Series in Mathematics, American Mathematical Society, vol.
1. The heuristic can easily be shown to converge within a ﬁnite number of steps. 3 Complexity Analysis The algorithm requires storage for the neighbor lists of all n data items, each of which has size proportional to that of the cluster to which it has been assigned. 2 The total space required is of order K i=1 |Ai | . Let μ and σ be respectively the mean and standard deviation of the cluster sizes; in terms of μ and σ, the space required is proportional to K(σ 2 + μ2 ). At the initialization step, the neighborhood list for each data item must be calculated.
Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings by Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi