Classification, Clustering, and Their Statistical Properties
Date of Graduation
Summer 2006
Degree
Master of Science in Mathematics
Department
Mathematics
Committee Chair
Yingcai Su
Abstract
This thesis analyzed three of the major topics inherent in multivariate statistical analysis, as well as introducing statistical theories that aid in the understanding of the topics that are discussed. There is also a focus on the statistical properties of these procedures. The first topic is classification. Classification is a procedure that attempts to place data from unknown populations into known populations based on either a pre-classified set of data, known as a training set, or certain knowledge about the populations. The second topic covered is clustering. In this technique, using statistical analysis, an attempt is made to find the underlying patterns within the data without the use of a training set. The final topic discussed is association rules. This topic finds items that occur together with a certain frequency.
Keywords
association rules, classification, clustering, multivariate statistical analysis, neural networks
Subject Categories
Mathematics
Copyright
© Benjamin J. Lakin
Recommended Citation
Lakin, Benjamin J., "Classification, Clustering, and Their Statistical Properties" (2006). MSU Graduate Theses/Dissertations. 2778.
https://bearworks.missouristate.edu/theses/2778
Dissertation/Thesis
