TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams

Authors:
Joel W. Reed;Yu Jiao;Thomas E. Potok;Brian A. Klump;Mark T. Elmore;Ali R. Hurson
Affiliations:
Oak Ridge National Laboratory, USA;Oak Ridge National Laboratory, USA;Oak Ridge National Laboratory, USA;Oak Ridge National Laboratory, USA;Oak Ridge National Laboratory, USA;The Pennsylvania State University, USA
Venue:
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Year:
2006

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Abstract

In this paper, we propose a new term weighting scheme called Term Frequency -- Inverse Corpus Frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods.