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Partitional Clustering Techniques for Multi-Spectral Image Segmentation
2007
Journal of Computers
Analyzing unknown data sets such as multispectral images often requires unsupervised techniques. Data clustering is a well known and widely used approach in such cases. Multi-spectral image segmentation requires pixel classification according to a similarity criterion. For this particular data, partitional clustering seems to be more appropriate. Classical K-means algorithm has important drawbacks with regard to the number and the shape of clusters. Probability density function based methods
doi:10.4304/jcp.2.10.1-8
fatcat:sqzfgslb5jda3kk4l6ptyen25u