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Article Information:
Title:
Binary k-nearest neighbor for text categorization
Author(s):
Songbo Tan
Journal:
Year:
2005
Volume:
Issue:
Page:
391 - 399
ISSN:
1468-4527
DOI:
10.1108/14684520510617839
Publisher:
Emerald Group Publishing Limited
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Abstract:
Purpose – With the ever-increasing volume of text data via the internet, it is important that documents are classified as manageable and easy to understand categories. This paper proposes the use of binary k-nearest neighbour (BKNN) for text categorization.
Design/methodology/approach – The paper describes the traditional k-nearest neighbor (KNN) classifier, introduces BKNN and outlines experiemental results.
Findings – The experimental results indicate that BKNN requires much less CPU time than KNN, without loss of classification performance.
Originality/value – The paper demonstrates how BKNN can be an efficient and effective algorithm for text categorization. Proposes the use of binary k-nearest neighbor (BKNN ) for text categorization.
Keywords:
Classification, Data handling, Information retrieval
Article Type:
General review