Categorization of web pages - Performance enhancement to search engine

@article{Lakshminarayana2009CategorizationOW,
  title={Categorization of web pages - Performance enhancement to search engine},
  author={Sadasivuni Lakshminarayana},
  journal={Knowl. Based Syst.},
  year={2009},
  volume={22},
  pages={100-104}
}
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References

SHOWING 1-10 OF 33 REFERENCES
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Web document clustering using hyperlink structures
TLDR
This paper applies the normalized cut clustering method developed in computer vision to the task of hyperdocument clustering, and experiments with normalized-cut method in the context of clustering query result sets for web search engines.
Discovering authorities and hubs in different topological Web graph structures
  • G. Meghabghab
  • Computer Science
    Proceedings International Conference on Information Technology: Coding and Computing
  • 2001
TLDR
The author believes that understanding the underlying Web page as a graph will help design better Web algorithms, enhance retrieval and Web performance, and recommends using graphs as part of a visual aid for search engine designers.
Search engines and Web dynamics
TLDR
Future evolution of the Web is discussed, and some important issues for search engines will be scheduling and query execution as well as increasingly heterogeneous architectures to handle the dynamic Web.
End user searching on the Internet: an analysis of term pair topics submitted to the excite search engine
TLDR
Subject area frequencies and their cooccurrences with one another were tallied and analyzed using hierarchical cluster analysis and multidimensional scaling, resulting in several well-defined high-level clusters of broad subject areas.
I 3 R: a new approach to the design of document retrieval systems
TLDR
A system that provides a number of FACILITIES and SEARCH STRATEGIES based on an EMPHASIS on domain knowledge used for refining the model of the information need, and the provision of a blowing mechanism that allows the user to NAVIGATE through the knowledge base.
Towards data modelling in information retrieval
TLDR
The motivations and the scope of the DIRD (Design of Information Retrieval Data) project, which is devoted to the development of an environ ment for the design of advanced applications of IR, are introduced and a new ER approach is introduced that extends the constructs of the ER model to manage the complexity of IR data.
I3R: A new approach to the design of document retrieval systems
TLDR
The system described in this article, 13R, provides a number of facilities and search strategies based on a detailed specification of the user’s information need and uses a novel architecture to allow more than one system facility to be used at a given stage of a search session.
...
1
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3
4
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