Publication Date

2017

Document Type

Dissertation/Thesis

First Advisor

Polansky, Alan M.

Degree Name

M.S. (Master of Science)

Department

Department of Statistics

LCSH

Statistics

Abstract

Density-based spatial clustering of applications with noise (DBSCAN) is one of the most popular clustering methods to classify nonlinearly grouped data. In particular, DNA methylations are considered to be differently skewed by CpG sites and to be nonlinearly grouped by cancer statuses. Under this circumstance, DBSCAN is expected to have a desirable clustering feature. This thesis reviews the DBSCAN algorithm and compares its performance to the other traditional clustering algorithm, K-means method. Simulation studies show the misclassification ratios of DBSCAN with the comparison of K-means method to evaluate their performance, and the classification of DNA methylations from patients with lung adenocarcinoma demonstrates the application of DBSCAN.

Comments

Advisors: Polansky, Alan.||Committee members: Nader Ebrahimi; Duchwan Ryu; Haiming Zhou.||Includes bibliographical references.||Includes illustrations.

Extent

iii, 26 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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