Comparative Study on Context-Based Document Clustering

Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. Objects in the same cluster should be as similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in the other clusters. Document clustering has become an increasingly important task in analysing huge documents. The challenging aspect to analyse the enormous documents is to organise them in such a way that facilitates better search and knowledge extraction without introducing extra cost and complexity. Document clustering has played an important role in many fields like information retrieval and data mining. In this paper, first Document Clustering has been proposed using Hierarchical Agglomerative Clustering and K-Means Clustering Algorithm.Here, the approach is purely based on the frequency count of the terms present in the documents where context of the documents are totally ignored. Therefore, the method is modified by incorporating Relatedness to measure the degree of relevance of the terms with respect to the concepts present in the documents. Thus, this Clustering is not only Term based but also understanding based (ie, Context Dependent). Next, the clustering is done by Hierarchical Agglomerative Clustering and K-Means with the Relatedness concept. Davies-Bouldin’s (DB) Index, which is a well-known metric, has been used to compare the quality of clusters-as they are obtained when the concept of Relatedness is not incorporated in the above mentioned document-clustering algorithms and secondly, when relatedness is integrated into the algorithms.