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An Empirical Study on Similarity between Documents in Nptel Application Using Clustering Techniques


S.Appavu Alias Balamurugan, N.Kalpana


Vol. 13  No. 12  pp. 58-65


This chapter presents a tutorial overview of the main clustering methods used in Data Mining. The goal is to provide a self-contained review of the concepts and the similarity underlying clustering techniques. The chapter begins by providing measures and criteria that are used for determining whether two objects are similar or dissimilar. Then the clustering methods are presented, divided into: hierarchical, partitioning, density-based, model-based, grid-based, and soft-computing methods. Following the methods, the challenges of performing clustering in large data sets are discussed. Finally, the chapter presents how to determine the number of clusters.


Clustering, K-means, Intra-cluster homogeneity, Inter-cluster separability