ENHANCING TEXT MINING USING ONTOLOGY BASED SIMILARITY DISTANCE MEASURE - Atiya Kazi; Priyanka Bandagale (International Journal of Innovations in Engineering Research and Technology, 2021)
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 sideinformation, by combining classical partitioning algorithms with probabilistic models. This will automatically boost the efficacy of clustering. Theclusters thus generated, can also be used as a training model to promote the solution of the classificationproblem. 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.