English, Article edition: Efficiency Considerations for Vertical kNN Text Categorisation Imad Rahal; Hassan Najadat; William Perrizo

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Bookmark: http://trove.nla.gov.au/version/133506
Physical Description
  • article
Language
  • English

Edition details

Title
  • Efficiency Considerations for Vertical kNN Text Categorisation
Author
  • Imad Rahal
  • Hassan Najadat
  • William Perrizo
Physical Description
  • article
Notes
  • 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 document d, and asked to find its subject matter or class label, Ci.In this paper, an optimised k-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 the k nearest neighbours to the new sample from the training space without a single database scan.
  • Text categorisation, text classification, text information management, P-trees, data mining
  • RePEc:wsi:jikmxx:v:05:y:2006:i:03:p:211-222
Language
  • English
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