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Enhanced Performance of Search Engine with Multi-Type Feature Co-Selection for Clustering Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 53 - Number 7 |
| Year of Publication: 2012 |
| Authors: K. Parimala, V. Palanisamy |
10.5120/8431-2203
|
K. Parimala, V. Palanisamy . Enhanced Performance of Search Engine with Multi-Type Feature Co-Selection for Clustering Algorithm. International Journal of Computer Applications. 53, 7 ( September 2012), 6-9. DOI=10.5120/8431-2203
Abstract
Information retrieval is a science of gathering information from unstructured data, the online information source i. e. , www. WWW contains data of heterogeneous types and of high dimension. Retrieving information from such database is a tedious work. Many researches are going on, to find a best optimal solution. A search engine is the tool for retrieving information from www. The internet helps the user to get the required information from www. A search engine respond to the user-need by answering their query, contains: Database, Web crawler, and Ranking algorithm. The optimality of the search engine is based on the ranking algorithm. The rank list is prepared based on the relevancy score. In this work we propose to use a novel algorithm, Multi-type Feature Co-selection for Clustering (MFCC) to the search engine as an alternative for the ranking algorithm. MFCC has proved its efficiency in clustering the heterogeneous web documentation.
References
- Christopher D. Meaning & et al. ; "An introduction to information retrieval"; Cambridge University Press, 2009.
- Ed. Green grass, "Information Retrieval: A survey"; 2000.
- Sew Staff, "How search engines work", 2007.
- Sergey Brie & Lawrence Page, "The Anatomy of a large-scale hyper textual web search engine" 2009.
- Lin Guoyuan & et al. , "Studies & evaluation on Meta search engines", 2011 IEEE, 978-1-61284-840-2/11.
- Joseph Williams and Ravi Starzi, "Tuning up the search engine", IT-PRO Jan/106-2011, 15 20-9202/01/2001 IEEE.
- Kristen L. Metzger, "Advanced web searching for the information professional".
- David Hawking, "Web search engines: part 1 & part 2", CSIRO/CT centre 2006; pg. 86-89, June 2006; "Computer: How things work" pg. 88489, Aug. 2006.
- K. Parimala, & V. Palanichamy, "A comparative study on search engines in information retrieval", ICSTAOR IT – 2006, XXVI ISPS Conference proceedings.
- Han & et al. , "Multi type feature co-selection for clustering for web documentation", IEEE transaction on knowledge engineering, June 2006.
- Srinivas M & et al. , "MFCC and ARM algorithms for text categorization", Aug 2010.
- Srinivas M & et al. , "Improving performance of Text categorization: Using MFCC and LSquare Machine Learning", 2010.
- Y. Yang and J. O. Pedersen, "A Comparative Study on feature Selection in Text categorization," Proc. Int'l Conf. Machine Learning (ICML '97), pp. 412-420, 1997.
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