Abstract
This paper presents a survey of Hybrid fuzzy c-means (FCM) clustering algorithms, The algorithmic steps, parameters involved in the algorithm & the experimental results on various datasets of several hybrid clustering methods are discussed in this paper. Hybrid FCM clustering techniques are obtained by modifying the FCM either by incorporating hesitation degree of Intuionistic approach or by replacing the Euclidean distance by the kernel induced distance or by considering the possibilistic information or by assigning a pixel to a cluster based on similarity information with certainity or uncertainity of Rough set method‥ Hybrid FCM technique is more robust to noise than the conventional FCM. A Novel hybrid clustering technique is also proposed in this paper whose performance will be more than the existing algorithms.
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