Date of Award
2003
Level of Access Assigned by Author
Open-Access Thesis
Degree Name
Master of Science in Electrical Engineering (MSEE)
Advisor
Habtom Ressom
Second Committee Member
Mohamed T. Musavi
Third Committee Member
Cristian Domnisoru
Abstract
This thesis presents a novel clustering technique known as adaptive double self- organizing map (ADSOM) that addresses the issue of identifying the "correct" number of clusters. ADSOM has a flexible topology and performs clustering and cluster visualization simultaneously, thereby requiring no a priori knowledge about the number of clusters. ADSOM combines features of the popular self-organizing map with two- dimensional position vectors, which serve as a visualization tool to decide the number of clusters. It updates its free parameters during training and it allows convergence of its position vectors to a fairly consistent number of clusters provided that its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automated detection of the number of clusters, thereby reducing human error that could be incurred from counting clusters visually. The reliance of ADSOM in identifying the number of clusters is proven by applying it to publicly available gene expression data from multiple biological systems such as yeast, human, mouse, and bacteria.
Recommended Citation
Wang, Dali, "Adaptive Double Self-Organizing Map for Clustering Gene Expression Data" (2003). Electronic Theses and Dissertations. 255.
https://digitalcommons.library.umaine.edu/etd/255