Durham Medical Books > Data Mining > Download e-book for kindle: Advances in Knowledge Discovery and Data Mining, Part I: by Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi

Download e-book for kindle: Advances in Knowledge Discovery and Data Mining, Part I: by Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi

By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi

ISBN-10: 3642136567

ISBN-13: 9783642136566

This e-book constitutes the complaints of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.

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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 resources 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

Definition 1. (Nearest Component): Let dp ∈ D and P = {Pi } where Pi ’s are components, the nearest component of dp from P , denoted by N Comp(dp, P ), is defined as the component for which the projection distance from dp is minimum. N Comp(dp, P ) = argmin(P Dist(dp, Pi )). The corresponding distance is the Pi ∈P “minimum projection”, denoted by M P roj(dp, P ) = min (P Dist(dp, Pi )) Pi ∈P Definition 2. (Farthest Candidate): Let P = {Pi } where Pi is a Partitionset. The farthest candidate of P , denoted by F Cand(P ), is defined as the data point for which the minimum projection distance is maximum.

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. 3, pp. 2225–2230 (2002) 9. : Mathematical Concepts and Novel Heuristic Methods for Data Clustering and Visualization.

Nguyen2 , James C. Bezdek2 , Christopher A. au Abstract. Given a pairwise dissimilarity matrix D of a set of n objects, visual methods (such as VAT) for cluster tendency assessment generally ˜ where the objects are reordered represent D as an n × n image I(D) to reveal hidden cluster structure as dark blocks along the diagonal of the image. A major limitation of such methods is the inability to high˜ when D contains highly complex clusters. light cluster structure in I(D) To address this problem, this paper proposes an improved VAT (iVAT) method by combining a path-based distance transform with VAT.

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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


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