Text Clustering With Using Side Information

Side information is present along with many text mining application. This side information may be provenance information, any links in the document, web logs which contain user access behavior, the links for any document or any other non textual attributes which are embedded into the text document. All these attributes may contain a large amount of information for clustering purposes. But it is difficult to calculate the concerned importance of this side information especially when some of the data is noisy. In that situation, it is risky to merge side information into the mining process because it can enhance the quality of the representation for the mining process or can add noise in this system. Thus, there should be a proper way to do this mining process so that it will make use of side information to maximize their advantages. Therefore, it is recommended to design an efficient algorithm which makes combination of classical portioning algorithm with probabilistic models in order to create an effective clustering approach.