Paper Details

Side information is available along with text document several text mining application. This sideinformation can be the link in the documents, web logs which contains user access behavior, provenance information, thelink for ant document or any other non-textual attributes which are embedded in text document. All these attributes maycontain huge amount of information for clustering purposes. Sometimes clustering more difficult when some of theinformation is noisy. In this matter it is inconvenient to merge side-information into the mining process because either itcan upgrade the quality of the representation for mining process or can add noise in this system. Thus, there should be aright way to do this mining process so that it will make use of side information to maximize their advantage. Therefore, itsuggests to design an efficient algorithm which makes combination of classical portioning algorithm with probabilisticmodels in order to create an effective clustering approach. Then the clustering approach will extend to classificationapproach for real data set which shows advantages of using such an approach.

Laxmi Mehetre, Durgesh Patil, Manish Pimple, Akshay Satkar, “ON THE USE OF SIDE INFORMATION FOR MINING TEXT DATA”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 3, pp. 431-433, March 2017.