A Survey on Meta Information Based Text Data Clustering and Classification
ISSN: 2278 1323 All Rights Reserved (c) 2015 IJARCET 2830 Abstract Text clustering is important problem in recent years due to the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. This problem finds number of applications in classification, visualization, document organization, collaborative filtering and indexing. The paper provides a detailed survey of the problem of text clustering. It discusses the key methods used for text clustering, and their relative advantages. The problem of classification has also been widely studied in the database, data mining, and information retrieval community's .The paper also provides a detail survey of a wide variety of text classification algorithms. In most cases, the data is not available only in the purely text form. Large amount of meta information is available along with the text documents. This meta information may be of different kinds, such as the links in the document or webpage, user access behavior from web logs or links or other non textual attributes which are embedded into the text document. Such unstructured attributes may contain a tremendous amount of information for clustering purposes. Therefore, this paper also focuses on approach which carefully ascertains the coherence of the clustering characteristics of the meta information with that of the text content. This will help in improving the quality of the clustering effects of both the text data and meta information. The paper then shows how to extend the clustering approach to the classification problem using the meta information of the text documents.