Text clustering has been recognized as an important component in data mining. Self-Organizing Map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper presents a new method of hierarchical text clustering based on combination ...

In order to make better business decisions, faster database browsing and reducing processing time of queries, Extraction of Information from text documents in efficient manner is needed. Clustering of huge number of text documents into different clusters, for better management of information, provides for a wide area in which a whole lot of research is currently being pursued. Recent developmen...

Text clustering divides a set of texts into clusters (parts), so that texts within each cluster are similar in content. It may be used to uncover the structure and content of unknown text sets as well as to give new perspectives on familiar ones. The main contributions of this thesis are an investigation of text representation for Swedish and some extensions of the work on how to use text clust...

With the growth of Internet, large amount of text data is increasing, which are created by different media like social networking sites, web, and other informatics sources, etc. This data is in unstructured format which makes it tedious to analyze it, so we need methods and algorithms which can be used with various types of text formats. Clustering is an important part of the data mining. Clust...

Clustering text documents into different category groups is an important step in indexing, retrieval, management and mining of abundant text data on the Web or in corporate information systems. Text clustering task can be intuitively described as finding, given a set vectors of some data points in a multi-dimensional space, a partition of text data into clusters such that the points within each...

Increasing progress in numerous research fields and information technologies, led to an increase the publication of papers. Therefore, researchers take a lot time find interesting papers that are close their field specialization. Consequently, this paper we have proposed documents classification approach can cluster text into meaningful categories which contain similar scientific field. Our pre...