Author Type

Graduate Student

Date of Award

2004

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Pandya, Abhijit S.

Abstract

K-means algorithm and Kohonen network possess self-organizing characteristics and are widely used in different fields currently. The factors that influence the behavior of K-means are the choice of initial cluster centers, number of cluster centers and the geometric properties of the input data. Kohonen networks have the ability of self-organization without any prior input about the number of clusters to be formed. This thesis looks into the performances of these algorithms and provides a unique way of combining them for better clustering. A series of benchmark problem sets are developed and run to obtain the performance analysis of the K-means algorithm and Kohonen networks. We have attempted to obtain the better of these two self-organizing algorithms by providing the same problem sets and extract the best results based on the users needs. A toolbox, which is user-friendly and written in C++ and VC++ is developed for applications on both images and feature data sets. The tool contains K-means algorithm and Kohonen networks code for clustering and pattern classification.

Extent

139 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 2004.|College of Engineering and Computer Science

Identifier

FADT13112

Format

pdf

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