Inverted Index based Modified Version of KNN for Text Categorization


Taeho Jo, Journal of Information Processing Systems Vol. 4, No. 1, pp. 17-26, Mar. 2008  

https://doi.org/10.3745/JIPS.2008.4.1.017
Keywords: String Vector, K- Nearest Neighbor, Text Categorization
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Abstract

This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.


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Cite this article
[APA Style]
Jo, T. (2008). Inverted Index based Modified Version of KNN for Text Categorization. Journal of Information Processing Systems, 4(1), 17-26. DOI: 10.3745/JIPS.2008.4.1.017.

[IEEE Style]
T. Jo, "Inverted Index based Modified Version of KNN for Text Categorization," Journal of Information Processing Systems, vol. 4, no. 1, pp. 17-26, 2008. DOI: 10.3745/JIPS.2008.4.1.017.

[ACM Style]
Taeho Jo. 2008. Inverted Index based Modified Version of KNN for Text Categorization. Journal of Information Processing Systems, 4, 1, (2008), 17-26. DOI: 10.3745/JIPS.2008.4.1.017.