An improved K-nearest-neighbor algorithm for text categorization

• Main Authors: JIANG, Shengyi, PANG, Guansong, WU, Meiling, KUANG, Limin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Text categorization; KNN text categorization; One-pass clustering; Spam filtering; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/7542; https://ink.library.smu.edu.sg/context/sis_research/article/8545/viewcontent/1_s2.0_S0957417411011511_main.pdf
• Institution: Singapore Management University

Text categorization is a significant tool to manage and organize the surging text data. Many text categorization algorithms have been explored in previous literatures, such as KNN, Naive Bayes and Support Vector Machine. KNN text categorization is an effective but less efficient classification method. In this paper, we propose an improved KNN algorithm for text categorization, which builds the classification model by combining constrained one pass clustering algorithm and KNN text categorization. Empirical results on three benchmark corpora show that our algorithm can reduce the text similarity computation substantially and outperform the-state-of-the-art KNN, Naive Bayes and Support Vector Machine classifiers. In addition, the classification model constructed by the proposed algorithm can be updated incrementally, and it has great scalability in many real-word applications. (C) 2011 Elsevier Ltd. All rights reserved.