A semantic approach for text document clustering using frequent itemsets and WordNet

  • Authors

    • Harsha Patil MANIT, BHOPAL, INDIA
    • Ramjeevan Singh Thakur MANIT, BHOPAL,INDIA
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.9.10220
  • Document Clustering, Frequent Item Sets, Semantic, Similarity Measures, WordNet.

  • Document Clustering is an unsupervised method for classified documents in clusters on the basis of their similarity. Any document get it place in any specific cluster, on the basis of membership score, which calculated through membership function. But many of the traditional clustering algorithms are generally based on only BOW (Bag of Words), which ignores the semantic similarity between document and Cluster. In this research we consider the semantic association between cluster and text document during the calculation of membership score of any document for any specific cluster. Several researchers are working on semantic aspects of document clustering to develop clustering performance. Many external knowledge bases like WordNet, Wikipedia, Lucene etc. are utilized for this purpose. The proposed approach exploits WordNet to improve cluster member ship function. The experimental result shows that clustering quality improved significantly by using proposed framework of semantic approach.

     

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  • How to Cite

    Patil, H., & Singh Thakur, R. (2018). A semantic approach for text document clustering using frequent itemsets and WordNet. International Journal of Engineering & Technology, 7(2.9), 102-105. https://doi.org/10.14419/ijet.v7i2.9.10220