A FUZZY CLUSTERING ENSEMBLE APPROACH FOR CATEGORICAL DATA
Journal Title: International Journal of Scientific Research and Management - Year 2013, Vol 1, Issue 6
Abstract
Data clustering is one of the essential tools for perceptive structure of a data set. It plays a crucial and initial role in machine learning, data mining and information retrieval. The intrinsic properties of the traditional algorithms intended for numerica l data, can be employed to measure distance between feature vectors and cannot be directly applied for clustering of categorica l da ta , Wherever domain value are distinct haven’t any ordering outlined. The final data partition generated by traditional algorithms, results in incomplete information and the core ensemble information matrix presents only cluster data point relat ions with many entries left unknown and disgrace the quality of the resulting cluster. In the proposed system, a new highly effective fuzzy cluster ensemble approach to categorical data clustering transforms the original categorical data matrix to an informat ion - pre serving numerical variation (QM), to which an effective hybrid graph partitioning technique can be directly applied. Using the fuzzy clustering algorithm, the quality matrix is determined efficiently and can be used to partition the categorical dat a under unsupervised circumstances
Authors and Affiliations
K. Lakshmi priya
Keywords
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