Clustering techniques are broadly used in researchare as where pattern recognition is needed, like in signal processing, automatic voice analysis, computer vision, and data mining. However, for each specific problem, the adequate technique must be selected in order to achieve better results. In this paper, a comparative analysis between the three mostly used clustering techniques (k-means, ISODATA, and the sequential clustering algorithm) is presented. The goal of the analysis is to compare the efficiency of each algorithm applied to numerical databases and images. The results of the application of the algorithms to a setof 25 images (natural and artificial) and 5 numerical databases are presented and discussed.
Analysis of Clustering Algorithms for Image Segmentation and Numerical Databases
2008-09-01
2315781 byte
Conference paper
Electronic Resource
English
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