logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Finding cluster centers and sizes via multinomial parameterization

delete2013-09-01
delete3
PRE
AI
S
Stan Lipovetsky *
DOI：10.1016/j.amc.2013.06.098delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
The clustering problem consists in dividing a data set into groups of observations that are similar within but different across. This paper presents a method for assessment the clusters centers and sizes in a non-linear least squares optimization with multinomial parameterization. The method is especially useful for large data sets as it operates on the summary statistics only. This approach also works for the problem of finding clusters' centers and sizes by the covariance matrix when the original data is not available. Estimation of the clusters centers and sizes can be followed by actual clustering. Example of application to marketing research problem is discussed. (C) 2013 Elsevier Inc. All rights reserved.
Keywords:
Clusters parameters
Nonlinear optimization
Multinomial parameterization

Journal

Applied Mathematics and Computation cover
Applied Mathematics and Computation
IF:
3.4
Papers:
2.3W
Citations:
3.3W

Organization

No organization information available