Centroid-based document classification: Analysis and experimental results

Abstract

Abstract. In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. Automatic text categorization, which is the task of as-signing text documents to pre-specified classes of documents, is an important task that can help both in organizing as well as in finding information on these huge resources. Text categorization presents unique challenges due to the large number of attributes present in the data set, large number of training samples, and attribute dependencies. In this paper we present a simple linear-time centroid-based docu-ment classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our experiments show that this centroid-based classifier consistently and substantially outperforms other algo-rithms such as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. Our analysis shows that the similarity measure used by the centroid-based scheme allows it to classify a new document based on how closely its be-havior matches the behavior of the documents belonging to different classes. This matching allows it to dynamically adjust for classes with different densities and accounts for dependencies between the terms in the different classes. We believe that this feature is the reason why it consistently outperforms other classifiers that cannot take into account these density differences and dependencies