Centroid-Based Document Classification: Analysis & Experimental Results

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Centroid-Based Document Classification: Analysis & Experimental Results (2000)

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by Eui-Hong (Sam) Han , George Karypis

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@INPROCEEDINGS{Han00centroid-baseddocument,
    author = {Eui-Hong (Sam) Han and George Karypis},
    title = {Centroid-Based Document Classification: Analysis & Experimental Results},
    booktitle = {},
    year = {2000},
    pages = {424--431},
    publisher = {}
}

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Abstract

In this paper we present a simple linear-time centroid-based document 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 algorithms 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 centroidbased scheme allows it to classify a new document based on how closely its behavior 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.

Keyphrases

analysis experimental result centroid-based document classification different class new document news source online text document similarity measure centroidbased scheme robust performance different density company-wide intranet tremendous growth wide range digital library centroid-based classifier naive bayesian

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