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In this paper, we present some numerical results of an experimental study of the problem of automatic determination of the number of clusters in unsupervised fuzzy clustering. The study was conducted using the well-known fuzzy c-means algorithm and four associated validity criteria that we applied to illustrative examples of artificial and real data sets. We will mainly focus on the risk of validating...
This survey gives state-of-the-art of genetic algorithm (GA) based clustering techniques. Clustering is a fundamental and widely applied method in understanding and exploring a data set. Interest in clustering has increased recently due to the emergence of several new areas of applications including data mining, bioinformatics, web use data analysis, image analysis etc. To enhance the performance...
A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by identifying and splitting a low-quality cluster. In the second phase,...
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