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.
Read 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
Th 2 where dist(dp, x) = feature j (dpj − xj ) . dpj and xj corresponds to the j of the data points dp and x respectively. At the beginning of the generation of partitions, there will be only one single point x without any principal component. In such cases, the Euclidean distance from the point x will be used as the projection distance. A “P artitionset” (denoted by Pi ) is defined as a set of datapoints which have lower projection distance to a particular component compared to the projection distance to any other component.
Finding correlation clusters in the arbitrary subspaces of highdimensional data is an important and a challenging research problem. The current state-of-the-art correlation clustering approaches are sensitive to the initial set of seeds chosen and do not yield the optimal result in the presence of noise. To avoid these problems, we propose RObust SEedless Correlation Clustering (ROSECC) algorithm that does not require the selection of the initial set of seeds. Our approach incrementally partitions the data in each iteration and applies PCA to each partition independently.
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. Fuzzy Automata and Decision Processes (1976) 8. : VAT: A tool for visual assement of (cluster) tendency. In: International Joint Conference on Neural Networks, vol.