Binary k‐nearest neighbor for text categorization

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.

Citation

Tan, S. (2005), "Binary k‐nearest neighbor for text categorization", Online Information Review, Vol. 29 No. 4, pp. 391-399. https://doi.org/10.1108/14684520510617839