Probabilistic Ranking of Documents Using Vectors in Information Retrieval

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


On the web, electronic form of information is increasing exponentially with the passage of past few years. Also, this advancement creates its own uncertainties. The overload information result is progressive while finding the relevant data with a chance of HIT or Miss Exposure. For improving this, Information Retrieval Ranking, Tokenization and Clustering techniques are suggestive as probable solutions. In this paper, Probabilistic Ranking using Vectors (PRUV) algorithm is proposed, in which tokenization and Clustering of a given documents are used to create more precisely and efficient rank gratify user’s information need to execute sharply reduced search, is believed to be a part of IR. Tokenization involves pre-processing of the given documents and generates its respective tokens and then based on probability score cluster are created. Performance of some of existing clustering techniques (K-Means and DB-Scan) is compared with proposed algorithm PRUV, using various parameters, e.g. Time, Accuracy and Number of Tokens Generated.


Information retrieval (IR) Ranking/indexing Tokenization Clustering 


  1. 1.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Book Co., New York (1983)MATHGoogle Scholar
  2. 2.
    Yates, R.B., Neto, B.R.: Modern Information Retrieval. ACM Press, Harlow (1999)Google Scholar
  3. 3.
    Dong, H., Husain, F.K., Chang, E.: A survey in traditional information retrieval models. In: IEEE International Conference on Digital Ecosystems and Technologies, pp. 397–402 (2008)Google Scholar
  4. 4.
    Jarvelin, K., Kekalainen, J.: IR methods for retrieving highly relevant documents. In: Proceedings of SIGIR, pp. 41–48 (2000)Google Scholar
  5. 5.
    Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. J. Am. Soc. Inf. Sci. 27, 129–146 (1976)CrossRefGoogle Scholar
  6. 6.
    Crestani, F., et al.: Is this document relevant? probably: a survey of probabilistic models in information retrieval. ACM Comput. Surv. 30(4), 528–552 (1998)CrossRefGoogle Scholar
  7. 7.
    Lashkari, A., Mahdavi, F., Ghomi, V.: A boolean model in information retrieval for search engines. In: IEEE International Conference on Information Management and Engineering, pp. 385–389 (2009)Google Scholar
  8. 8.
    Raman, S., Kumar, V., Venkatesan, S.: Performance comparison of various information retrieval models used in search engines. In: IEEE Conference on Communication, Information and Computing Technology, Mumbai (2012)Google Scholar
  9. 9.
    Qui, J., Tang, C.: Topic oriented semi-supervised document clustering. In: Proceedings of SIGMOD, Workshop on Innovative Database Research, pp. 57–62 (2007)Google Scholar
  10. 10.
    Karthikeyan, M., Aruna, P.: Probability based document clustering and image clustering using content-based image retrieval. J Appl Soft Comput 13, 959–966 (2012)CrossRefGoogle Scholar
  11. 11.
    Wang, J., Su, X.: An improved k-means clustering algorithm. In: IEEE Conference on Communication Software and Networks, Japan, pp. 44–46 (2011)Google Scholar
  12. 12.
    Senthesree, K., Daodaran, A., Appaji, S., Devi, D.N.: Web usage data clustering using DBSCAN algorithm and set similarities. In: IEEE Conference on Data Storage and Data Engineering, India, pp. 220–224 (2010)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Engineering DepartmentNIT KurukshetraChandigarhIndia

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