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Efficiency Considerations for VerticalkNN Text Categorisation

Author

Listed:
  • Imad Rahal

    (211, Peter Engel Science Center, Computer Science Department, College of St. Benedict and St. John's University, Collegeville, MN 56321, USA)

  • Hassan Najadat

    (Computer Information Systems Department, Jordan University of Science and Technology, P.O. Box 3030 Irbid, 22110, Jordan)

  • William Perrizo

    (IACC 258 A15, Computer Science Department, North Dakota State University, Fargo, ND 58105, USA)

Abstract

The importance of text mining stems from the availability of huge volumes of text databases holding a wealth of valuable information that needs to be mined. Text mining is a coarse area encompassing many finer branches one of which is text categorisation or text classification. Text categorisation is the process of assigning class labels to documents based entirely on their textual contents where we are given a documentd, and asked to find its subject matter or class label,Ci.In this paper, an optimisedk-Nearest Neighbours classifier that uses discretisation, the P-tree technology, and dimensionality reduction to achieve a high degree of accuracy, space utilisation and time efficiency is proposed. One of the fundamental contributions of this work is that as new samples arrive, the proposed classifier can find theknearest neighbours to the new sample from the training space without a single database scan.

Suggested Citation

  • Imad Rahal & Hassan Najadat & William Perrizo, 2006. "Efficiency Considerations for VerticalkNN Text Categorisation," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 211-222.
    • Handle: RePEc:wsi:jikmxx:v:05:y:2006:i:03:n:s021964920600144x
    DOI: 10.1142/S021964920600144X
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