Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
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As a simple, effective and nonparametric classification method, k Nearest Neighbor (KNN) is widely used in document classification for dealing with the much more difficult problem such as large-scale or many of categories. But KNN classifier may have a problem when training samples are uneven. The problem is that KNN classifier may decrease the precision of classification because of the uneven density of training data. To solve the problem, a new clustering-based KNN method is presented in this paper. It preprocesses training data by using clustering, then classify with a new KNN algorithm, which adopts a dynamic adjustment in each iteration for the neighborhood number K. This method would avoid the uneven classification phenomenon and reduce the misjudgment of the boundary testing samples. We have an experiment in text classification and the result shows that it has good performance.