Degree Type

Dissertation

Date of Award

2008

Degree Name

Doctor of Philosophy

Department

Statistics

First Advisor

Ranjan Maitra

Abstract

New methodology is proposed for the clustering of noisy and directional data. The dissertation contains three separate research papers. The first provides an

efficient k-means type algorithm for clustering observations in the presence of scattered observations. Scattered observations are defined as unlike any other, so traditional approaches that force them into groups can lead to erroneous conclusions. The second paper develops a computationally efficient k-means algorithm for grouping observations that lie on the surface of a high dimensional sphere. The final paper builds off the first two to develop an

algorithm that clusters directional data in the presence of scatter.

DOI

https://doi.org/10.31274/etd-180810-3075

Copyright Owner

Ivan Peter Ramler

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

135 pages

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