In this paper we review studies of the growth of the Internet and technologies that are useful
for information search and retrieval on the Web. We present data on the Internet from several …

Because of large amounts of unstructured text data generated on the Internet, text mining is
believed to have high commercial value. Text mining is the process of extracting previously …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

This paper presents the results of an experimental study of some common document
clustering techniques. In particular, we compare the two main approaches to document …

Clustering is a powerful technique for large-scale topic discovery from text. It involves two
phases: first, feature extraction maps each document or record to a point in high …

Finding clusters in data, especially high dimensional data, is challenging when the clusters
are of widely differing shapes, sizes, and densities, and when the data contains noise and …

Users of Web search engines are often forced to sift through the long ordered list of
document “snippets” returned by the engines. The IR community has explored document …

We propose a new algorithm capable of partitioning a set of documents or other samples
based on an embedding in a high dimensional Euclidean space (ie, in which every …

Document clustering techniques mostly rely on single term analysis of the document data
set, such as the vector space model. To achieve more accurate document clustering, more …

Feature selection methods have been successfully applied to text categorization but seldom
applied to text clustering due to the unavailability of class label information. In this paper, we …