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@article{146585,
        author = {Mamta Rani and Gagan Dhawan },
        title = {A Noval Approach for Document categorization Based on Latent Sementic Indexing },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {1},
        pages = {175-179},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146585},
        abstract = {The intensive expansion of the web and the enlarged number of users has forced new organizations to place their processed data on the web. Besides all this, the constant development in Internet usage is enhancing the problems in controlling the information. The swift dominance of World Wide Web relevance and the want to arrange the data efficiently, to look up the data for knowledge, have emphasized to develop more intellectual and efficient real time web clustering algorithms [8].Latent Semantic Indexing is a better textual representation technique as it maintains semantic information between the words. Hence, we used the singular valuedecomposition (SVD) methods to extract the textual features based on LSI.The LSI also knew LSA. In our experiments, we conducted comparison between some of the well –known classification methods such as Naïve Bayes, k-Nearest Neighbours,NeuralNatwork, Random Forest, Support Vector Machine, classification tree. A NovelApproch for document categorization  based on LSI  in which initially start work on  contains Topic  and then Topic contains the folders and folders contains categories after that a document will  be created .},
        keywords = {Document Categorization, Tokenizing,  preproccessing,Term Finding, VSM(Vactor Space Modle),Clustring,LSA or LSI, SOM},
        month = {},
        }