Mining Query Facets from the Search Results
A. Mahalakshmi1, T. Yawanikha2, D. Bhanu3, V. P. Arul Kumar4, K. M. Murugesan5
1A. Mahalakshmi, Assistant Professor, Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore (Tamil Nadu), India.
2T. Yawanikha, Professor, Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore (Tamil Nadu), India.
3D. Bhanu, Assistant Professor, Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore (Tamil Nadu), India.
4V. P Arul Kumar, Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore (Tamil Nadu), India.
5K. M. Murugesan, Professor, Department of Computer Science and Engineering, Karpagam Institute of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 26 May 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 26 June 2019 | PP: 249-256 | Volume-8 Issue-1S5 June 2019 | Retrieval Number: A00440681S519/2019©BEIESP
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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: Mining Query Facets includes multiple groups of words or phrases that are obtained from the query given in the search engine. The problem in addressing one important aspects of query facets related to the query given in the search engine is solved by QDMiner technique. The proposed system automatically extracts the query facets obtained from the top search results using the three HTML patterns. The main objective of the proposed system analyzes the problem in list duplication by aggregating the lists obtained in the search engine. The similarities between the extracted lists are estimated by k-means clustering technique which helps to avoid duplication among the websites. Mining facets will automatically rank all the lists extracted from the search results and display the higher priority lists in the top search results without any duplication and it also reduces the user the searching time to obtain better knowledge.
Keywords: Query Facet, Higher Priority list, Duplication, Knowledge.
Scope of the Article: Data Mining