Abstract/Details

Improved statistical methods for <i>k</i>-means clustering of noisy and directional data


2008 2008

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Abstract (summary)

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.

Indexing (details)


Subject
Statistics
Classification
0463: Statistics
Identifier / keyword
Pure sciences; Directional data; Noisy data; k-means clustering
Title
Improved statistical methods for <i>k</i>-means clustering of noisy and directional data
Author
Ramler, Ivan Peter
Number of pages
135
Publication year
2008
Degree date
2008
School code
0097
Source
DAI-B 70/01, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549997719
Advisor
Maitra, Ranjan
Committee member
Carriquiry, Alica; Meeker, William; Nettleton, Dan; Steward, Brian
University/institution
Iowa State University
Department
Statistics
University location
United States -- Iowa
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3342282
ProQuest document ID
276044684
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/276044684
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