A Link-Based Cluster Collection Approach Combined Contagious Cluster With For Categorical Data Clustering

Data clustering is a challenging task in data mining technique. Various clustering algorithms are developed to cluster or categorize the datasets. Many algorithms are used to cluster the categorical data. Some algorithms cannot be directly applied for clustering of categorical data. Several attempts have been made to solve the problem of clustering categorical data via cluster ensembles. But these techniques generate a final data partition based on incomplete information. The ensemble information matrix represents cluster relations with many unknown entries. The link based ensemble approach has been established with the ability to discover unknown values and improve the accuracy of the data partition. Besides clustering, similarity based ranking approach, HITS link analysis is also proposed to enhance the categorical results. This enhanced link-based clustering and ranking method almost outperforms both predictable clustering algorithms for categorical data and contagious cluster ensemble techniques for grade.