ICCCES-16

ENHANCING TEXT MINING USING ONTOLOGY BASED SIMILARITY DISTANCE MEASURE (140602)

DOI :

Abstract : Generally,Text mining applications disregard the side-information contained within the text document,which can enhance the overall clustering process. To overcome this deficiency,the proposed algorithm will work in two phases. In the first phase,it will perform clustering of data along with the side information,by combining classical partitioning algorithms with probabilistic models. This will automatically boost the efficacy of clustering. The clusters thus generated,can also be used as a training model to promote the solution of the classification problem. In the second phase,a similarity based distance calculation algorithm,which makes use of two shared word spaces from the DISCO ontology,is employed to perk up the clustering approach. This pre-clustering technique will calculate the similarity between terms based on the cosine distance method,and will generate the clusters based on a threshold. Th is inclusion of ontology in the pre-clustering phase w ill generate more coherent clusters by inducing ontology along with side-information.

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Downloads : 1365

Publication Date :

Modified Date : 2016-01-30

Cite/Export :

Atiya Kazi , Priyanka Bandagale , "ENHANCING TEXT MINING USING ONTOLOGY BASED SIMILARITY DISTANCE MEASURE", IJIERT - International Journal of Innovations in Engineering Research and Technology, ICCCES-16, ISSN : 2394-3696, Page No.