Binary k-nearest neighbor for text categorization.

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. [ABSTRACT FROM AUTHOR]