JSM 2004 - Toronto

Abstract #301150

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Activity Number: 90
Type: Contributed
Date/Time: Monday, August 9, 2004 : 9:00 AM to 10:50 AM
Sponsor: SSC
Abstract - #301150
Title: A Generative Approach to Dendrograms with Application to Functional Biomechanical Data
Author(s): Jeffrey A. Bakal*+ and Glen Takahara and John T. Smith and Joan Stevenson
Companies: Queen's University and Queen's University and Queen's University and Queen's University
Address: Room 310, Jeffery Hall, Kingston, ON, K7L 3N6, Canada
Keywords: cluster analysis ; functional data analysis ; biomechanics
Abstract:

Classical hierarchical clustering is a common method for the unsupervised classification of data. An attractive feature of this method is the graphical representation of the clustering, the dendrogram, which facilitates estimation of the number of clusters as well as providing certain information about the clusters. Euclidean distance is not always appropriate for representing dissimilarity between objects and clusters. We propose a dissimilarity measure based on the joint probability of membership in the same cluster, computed in terms of an estimated multivariate normal mixture model. The advantages of this method are pertinent to the analysis of functional data and are illustrated in the context of functional data arising in biomechanics.

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