Survey on Enhancing Clustering Output using Side Information by the Textual Extraction Mechanism

Abstract

In text mining, more operations are based on the statistical analysis of a term, word or phrase. Clustering is a popular technique for automatically organizing a large collection of text; it is also used to text classification. Many text mining applications contains side information with text documents in the form of web documents, user access web-log, and different links attached with text files. This side information is helpful for clustering purpose but sometime it is risky to use side information because it may add noise to procedure. So we need a better technique for text mining to improve quality of presentation. In this paper, we are using different algorithms for enhancement of the clustering quality with the document-based, sentence-based, corpus-based, and combined approach concept analysis design, so as to maximize the benefits from using side information.