JSM 2004 - Toronto

Abstract #302010

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Activity Number: 90
Type: Contributed
Date/Time: Monday, August 9, 2004 : 9:00 AM to 10:50 AM
Sponsor: Biometrics Section
Abstract - #302010
Title: A Gap-statistic-based Method of Determining the Number of Clusters
Author(s): Mingjin Yan*+
Companies: Virginia Polytechnic Institute and State University
Address: 1776 Liberty Lane Apt. C34, Blacksburg, VA, 24060,
Keywords: Gap statistic ; K-means ; clustering
Abstract:

Cluster analysis has attracted increasing attention as an exploratory tool in the "unsupervised" learning of gene expression data. Currently, the major concerns in clustering analysis lie in two aspects: what clustering method should be implemented and how to determine the number of clusters in a dataset. In spite of its importance, making inference about the correct group number in a dataset is not an easy task due to no clear definition of cluster." Widely used clustering algorithms (e.g., K-means or hierarchical) give heuristic but no determined results about the number of groups. Tibshirani et al. proposed a method which formalized the estimation of the number of data clusters via the Gap statistic. A new technique based on the Gap statistic is developed to find the optimal cluster number. Performances of this new method and the Gap statistic are compared with both simulated and real-world datasets.

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