Award Date

5-1-2015

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Laxmi P. Gewali

Second Committee Member

John T. Minor

Third Committee Member

Ajoy K. Datta

Fourth Committee Member

Henry Selvaraj

Number of Pages

54

Abstract

Partitioning a given set of points into clusters is a well known problem in pattern recognition, data mining, and knowledge discovery. One of the well known methods for identifying clusters in Euclidean space is the K-mean algorithm. In using the K-mean clustering algorithm it is necessary to know the value of k (the number of clusters) in advance. We propose to develop algorithms for good estimation of k for points distributed in two dimensions. The techniques we pursue include a bucketing method, g-hop neighbors, and Voronoi diagrams. We also present experimental results for examining the performances of the bucketing method and K-mean algorithm.

Keywords

Bucketing Approach; Cluster analysis; Clustering; Computational Geometry; G-hop Approach; K-Means Clustering; Pattern perception; Voronoi Diagram based G-hop

Disciplines

Computer Sciences | Geometry and Topology | Theory and Algorithms

File Format

pdf

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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