Hierarchical Semantic Relational Coverage Measure Based Web Document Clustering Using Semantic Ontology
B. Selvalakshmi1, M. Subramaniam2
1B.Selvalakshmi, Assistant Professor, Dept. of CSE, Tagore Engineering College, Chennai, India.
2Dr.M.Subramaniam, Professor , S.A.Engineering College, Chennai – 600077., India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 891-896 | Volume-8 Issue-10, August 2019 | Retrieval Number: J90620881019/2019©BEIESP | DOI: 10.35940/ijitee.J9062.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The problem of web document clustering has been well studied. Web documents has been grouped based various features like textual, topical and semantic features. Number of approaches has been discussed earlier for the clustering of web documents. However the method does not produce promising results towards web document clustering. To overcome this, an efficient hierarchical semantic relational coverage based approach is presented in this paper. The method extracts the features of web document by preprocessing the document. The features extracted have been used to measure the semantic relational coverage measure in different levels. As the documents are grouped in a hierarchical manner, the method estimates the relational coverage measure in each level of the cluster. Based on the semantic relational measure at different level, the method estimates the topical semantic support measure. Using these two, the method computes the class weight. The estimated class weight has been used to perform document clustering. The proposed method improves the performance of document clustering and reduces the false classification ratio.
Keywords: Web Semantics, Semantic Ontology, Clustering, Hierarchical Clustering. SRC, TSS.
Scope of the Article: Web Semantics