Master of Science in Computer Science
First Committee Member
Laxmi P. Gewali
Second Committee Member
John T. Minor
Third Committee Member
Ajoy K. Datta
Fourth Committee Member
Number of Pages
Partitioning a given set of points into clusters is a well known problem in pattern recognition, data mining, and knowledge discovery. One of the well known methods for identifying clusters in Euclidean space is the K-mean algorithm. In using the K-mean clustering algorithm it is necessary to know the value of k (the number of clusters) in advance. We propose to develop algorithms for good estimation of k for points distributed in two dimensions. The techniques we pursue include a bucketing method, g-hop neighbors, and Voronoi diagrams. We also present experimental results for examining the performances of the bucketing method and K-mean algorithm.
Bucketing Approach; Cluster analysis; Clustering; Computational Geometry; G-hop Approach; K-Means Clustering; Pattern perception; Voronoi Diagram based G-hop
Computer Sciences | Geometry and Topology | Theory and Algorithms
University of Nevada, Las Vegas
K C, Sanjeev, "Efficient Estimation of Cluster Population" (2015). UNLV Theses, Dissertations, Professional Papers, and Capstones. 2370.
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