Assignment3_answer

Assignment3_answer - Model Answer: Question 1: (a) 1....

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Question 2: (1) Most current clustering methods can work well for low dimensional data but don’t work efficiently for high dimensional data because of the inherent sparsity of the data known as the dimensionality curse. In high dimensional space, the distance between every pair of points is almost the same for a wide variety of data distributions and distance functions. Under such circumstances, even the meaningfulness of proximity of clustering in high dimensional data may be called into problem. On solution is to use feature selection in order to reduce the dimensionality of the space. (2) The ORCLUS algorithm provided with the parameter number of cluster(k) and number of dimensions(l) and in order to find k clusters, each in l-dimensional subspace. And the ORCLUS algorithm operates iteratively. For each iteration, the
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This note was uploaded on 04/23/2010 for the course CSC CSC5120 taught by Professor Adafu during the Fall '09 term at CUHK.

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Assignment3_answer - Model Answer: Question 1: (a) 1....

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