Download Advances in Knowledge Discovery and Data Mining: 14th by Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. PDF

By Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)

This e-book constitutes the lawsuits 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: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I PDF

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Additional info for Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I

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2. The points in the correlation cluster must be closer (< ) to its principal component. 3 The ROSECC Algorithm In this section, we will describe the details of the proposed ROSECC algorithm and also analyze the effect of different parameter values in the algorithm. The overall procedure is shown in Algorithm 1. 1 Algorithm Description The five different steps in the ROSECC algorithm are described below.

25]. Assume that two M point sets are U = {ui }N i=1 and V = {vi }i=1 , the chamfer distance is defined as dcham (U, V) = 1 N min ui − vj . ui ∈U vj ∈V The symmetric chamfer distance can be obtained by adding dcham (V, U). The chamfer distance between two shapes can be efficiently computed using a distance transform (DT, Figure 3(f)), which takes a binary image as input, and assigns to each pixel in the image the distance to its nearest feature. We use Canny edges as image feature points (Figure 3(e)) and the Euclidean distance for DT, and the model points are the projected contours of a 2D (rigid) square template.

NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. References 1. : Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput. 22(11), 1025–1034 (1973) 2. : Rock: A robust clustering algorithm for categorical attributes. Information Systems, 512–521 (1999) 3. : Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data.

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