By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
This ebook constitutes the court cases of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
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Additional info for Advances in Knowledge Discovery and Data Mining, Part I: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabat, India, June 21-24, 2010, Proceedings
Houle Table 1. 2 Image Data We tested the clustering algorithms on the Amsterdam Library of Object Images (ALOI) , which consists of 110,250 images of 1000 common objects. Each image is represented by a dense 641-dimensional feature vector based on color and texture histograms (see  for details on how the vectors were produced). The following data sets were used: – I1-ALOI-var: A subset of 13943 images, generated by selecting objects unevenly from among the classes, with the i-th object class having approximately 40000/(400 + i) image instances selected.
Sometimes, hierarchical structure can be detected by the presence of diagonal sub-blocks within larger diagonal blocks. 6 4 2 0 -2 -4 -6 -8 -6 -4 -2 0 2 4 6 8 (a) (b) (c) Fig. 1. An example of the VAT algorithm 3 Improved VAT (iVAT) At a glance, a viewer can estimate the number of clusters c from a VAT image by counting the number of dark blocks along the diagonal if these dark blocks possess visual clarity. However, this is not always possible. Note that a dark block appears only when a compact group exists in the data.
1 correct and 4 closer for aVAT, and 1 correct and 3 closer for both CCE and DBE). 3. Speciﬁcally, when using iVAT images, aVAT, CCE and DBE yield the same estimate for the Iris and Action data sets. They all yield acceptable (but diﬀerent) estimates for the Gene and Face data sets. They disagree for the Vote and MF data sets. Overall, these three methods are comparable to each other and there is no clear winner (at least based on the results on these data sets used currently). However, we can see that the positions of peaks and valleys in the projection signal in DBE implicitly correspond to centers and ranges of sub-blocks (or clusters).
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