Masters Theses

Keywords and Phrases

Hierarchical clustering; Subjective clustering

Abstract

"In order to develop successful products in today’s competitive market, it is essential to analyze customer needs accurately. Because the number of customer needs collected using a marketing survey tends to be large, it is necessary to group similar needs and identify a small number of representative needs. Subjective Clustering is a statistical method that uses the Hierarchical Clustering algorithm to group similar customer needs into clusters. This research compares Subjective Clustering (SC) and Bootstrap applied to Subjective Clustering (BS-SC) for determining how accurately these methods estimate population primary clusters. In order to investigate inherent characteristics in the two methods, the first paper proposes a simulation-based comparative study. The research done in the second paper validates the simulation- based research with empirical data-based research, in which the authors recruited participants to perform the clustering experiments and assumed that these participants made up the population under study. The authors randomly sampled subsets of participants for the desired comparative study. Both sets of results suggest that BS-SC estimates the population primary cluster more accurately than SC when the sample size is small relative to the population size; however, as the sample size increases, SC estimates the population primary cluster more accurately then BS-SC"--Abstract, page iv.

Advisor(s)

Takai, Shun

Committee Member(s)

Midha, A. (Ashok)
Du, Xiaoping

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering

Comments

The author would like to thank the Intelligent Systems Center and the Interdisciplinary Engineering Department at the University of Missouri--Rolla for supporting this research.

Research Center/Lab(s)

Intelligent Systems Center

Publisher

University of Missouri--Rolla

Publication Date

Fall 2006

Journal article titles appearing in thesis/dissertation

  • A comparative study of bootstrap application to subjective clustering
  • Investigating the accuracy of subjective clustering and bootstrap application to subjective clustering using an empirical population

Pagination

x, 48 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2006 Nishant Bhardwaj, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Subject Headings

Bootstrap (Statistics)Cluster analysis

Thesis Number

T 9103

Print OCLC #

124068084

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