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 numerical data, can be employed to measure distance between feature vectors and cannot be directly applied for clustering of categorical data ,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 relations 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 informationpreserving 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 data under unsupervised circumstances.