Polansky, Alan M.
M.S. (Master of Science)
Department of Statistics
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.
Alghzzy, Mohammed Atef, "Density-based spatial clustering of applications with noises for DNA methylation data" (2017). Graduate Research Theses & Dissertations. 2066.
iii, 26 pages
Northern Illinois University
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