Grouping documents based on their textual content is an important application of clustering
referred to as text clustering. This paper deals with two issues in text clustering which are the …

The use of learning algorithms for text classification assumes the availability of a large
amount of documents which have been organized and labeled correctly by human experts …

One of the fastest growing research fields in recent times is Document Clustering. It gained
its importance in text mining due to the tremendous increase of documents on internet …

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Objective of the document clustering techniques is to assemble similar documents and
segregate dissimilar documents. Unlike document classification, no labeled documents are …

Document clustering is a widely used strategy for information retrieval and text data mining.
In traditional document clustering systems, documents are represented as a bag of …

Given a large unlabeled document collection, the aim of this paper is to develop an accurate
and efficient algorithm for solving the clustering problem over this collection. Document …

Document clustering has the goal of discovering groups with similar documents. The
success of the document clustering algorithms depends on the model used for representing …

Text clustering is one of the important research issues in the field of text mining, where the
documents are grouped without predefined categories or labels. In literature we can find a …

This paper addresses the problem of learning to classify texts by exploiting information
derived from clustering both training and testing sets. The incorporation of knowledge …