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Adaptive Clustering Techniques and Their Applications
Abstract
With the increasing volume of data, developing techniques to handle it has become the need of the hour. One such efficient technique is clustering. Data clustering is under vigorous development. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Several data clustering algorithms have been developed in this regard. Data is uncertain and vague. Hence uncertain and hybrid based clustering algorithms like fuzzy c means, intuitionistic fuzzy c means, rough c means, rough intuitionistic fuzzy c means are being used. However, with the application and nature of data, clustering algorithms which adapt to the need are being used. These are nothing but the variations in existing techniques to match a particular scenario. The area of adaptive clustering algorithms is unexplored to a very large extent and hence has a large scope of research. Adaptive clustering algorithms are useful in areas where the situations keep on changing. Some of the adaptive fuzzy c means clustering algorithms are detailed in this chapter.
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