NDLI: TF-SIDF: Term frequency, sketched inverse document frequency

Content Provider	IEEE Xplore Digital Library
Author	Baena-Garcia, M. Carmona-Cejudo, J.M. Castillo, G. Morales-Bueno, R.
Copyright Year	2011
Description	Author affiliation: Dpto. Lenguajes y Ciencias de la Computation, Universidad de Malaga, Spain (Baena-Garcia, M.; Carmona-Cejudo, J.M.; Morales-Bueno, R.) \|\| Department of Mathematics, University of Aveiro, Portugal (Castillo, G.)
Abstract	Exact calculation of the TF-IDF weighting function in massive streams of documents involves challenging memory space requirements. In this work, we propose TF-SIDF, a novel solution for extracting relevant words from streams of documents with a high number of terms. TF-SIDF relies on the Count-Min Sketch data structure, which allows to estimate the counts of all the terms in the stream. Results of the experiments conducted with two dataset show that this sketch-based algorithm achieves good approximations of the TF-IDF weighting values (as a rule, the top terms with highest TF-IDF values remaining the same), while substantial savings in memory usage are observed. It is also observed that the performance is highly correlated with the sketch size, and that wider sketch configurations are preferable given the same sketch size.
Starting Page	1044
Ending Page	1049
File Size	348543
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121796
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Graphics count-min sketch Correlation Radiation detectors Data structures text mining tfidf Approximation methods Intelligent systems
Content Type	Text
Resource Type	Article

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