A Survey on Effective Quality Enhancement of Text Clustering&Classification Using METADATA

Text clustering has become more important problem recently because of the large amount of unstructured information which is accessible in many forms in online forums such as the web, online networks, and other information networks. In a lot of cases, the information is not purely available in text form. A lot of side-information is available along with the text documents. Such side-information may be of altered kinds, such as the links in the document, user-access behaviour from web logs, or added non-textual attributes which are embedded into the text document. Such attributes may contain a large amount of data for clustering purposes. However, the data relativity of this side-information may be difficult to estimate, abnormally if some of the information is noisy. In such cases, it can be chancy to absorb side information into the clustering technique, because it can either improve the superior of the representation for clustering, or can add noise to the process. Therefore, we charge a conscionable way to perform the clustering technique, so as to aerate the advantages from application this side information. In this paper, we survey on side information for improving the text mining technique.