This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 636
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #309259
Title: Identifying General-Shaped Clusters Using the k-Means Algorithm
Author(s): Anna D. Peterson*+ and Ranjan Maitra and Arka Ghosh
Companies: Iowa State University and Iowa State University and Iowa State University
Address: 1640 Carroll Ave, Ames, IA, 50010, USA
Keywords: k-means ; single linkage ; clustering
Abstract:

The objective of clustering is to separate observations into groups such that observations within each group are similar in some sense but different from observations in other groups. We present a computationally practical non-parametric clustering approach to identify general-shaped clusters that is applicable to large datasets and multiple dimensions. Specifically, we first fit a k-means algorithm to the dataset assuming a very large number of clusters K > (presumably much larger than the true number of clusters). The advantage of using k-means is its superior computational efficiency on large datasets, however, the algorithm is designed to identify homogeneous spherically-shaped clusters in an ideal situation. We therefore identify general-shaped clusters by using a separability index to merge groups that are close together. The resulting clustering is our final derived grouping.


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