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In this paper at first a new measure of stability of clustering solutions over different bootstrap samples of a data set is proposed. Thereafter in this paper, a multiobjective optimization based clustering technique is developed which optimizes both the measures of symmetry and stability simultaneously to automatically determine the appropriate number of clusters and the appropriate partitioning...
An important approach for landcover classification in remote sensing images is by clustering the pixels in the spectral domain into several fuzzy partitions. In this article the problem of fuzzy clustering is posed as one of searching for some suitable number of cluster centers so that some measures of validity of the obtained partitions should be optimized. In this paper a recently developed multiobjective...
Most clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting...
An important approach for landcover classification in remote sensing images is by clustering the pixels in the spectral domain into several partitions. In this article the problem of clustering is posed as one of searching for some suitable number of cluster centers so that some measures of validity of the obtained partitions should be optimized. In this paper a new multiobjective clustering technique...
This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution...
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