A Comparative Study of Bootstrap Application to Subjective Clustering
Abstract
In order to develop successful products in today's competitive market, it is essential to analyze customer needs accurately. Since the number of customer needs collected using 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 Hierarchical Clustering algorithm to group similar customer needs into clusters. This paper 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, this paper proposes a simulation-Based comparative study. the result suggests that BS-SC estimates population primary cluster more accurately than SC when the sample size is small relative to population size; however, as sample size increases, SC estimate is more accurate than BS-SC. Copyright © 2006 by ASME.
Recommended Citation
N. Bhardwaj and S. Takai, "A Comparative Study of Bootstrap Application to Subjective Clustering," Proceedings of the ASME Design Engineering Technical Conference, vol. 2006, American Society of Mechanical Engineers, Jan 2006.
The definitive version is available at https://doi.org/10.1115/detc2006-99623
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Bootstrap; Comparative study; Customer needs; Hierarchical clustering; Subjective clustering
International Standard Book Number (ISBN)
978-079183784-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Society of Mechanical Engineers, All rights reserved.
Publication Date
01 Jan 2006
