NDLI: Document clustering based on time series

Content Provider	IEEE Xplore Digital Library
Author	Matei, L.S. Trausan-Matu, S.
Copyright Year	2015
Description	Author affiliation: Fac. of Autom. Control & Comput. Sci., Univ. Politeh. of Bucharest, Bucharest, Romania (Matei, L.S.; Trausan-Matu, S.)
Abstract	This paper presents a novel document clustering algorithm that represents documents as a time series of words. Document clustering is very important due to the fact that it permits us to group them based on some certain criteria, especially nowadays when a large number of articles are available. The timed series representation of the document instead of the vector model permits us to consider a new algorithm for the computation of the distance between documents: dynamic time warping. This novel representation together with the dynamic time warping algorithm represents the foundation for computing the similarity and the clustering of the documents. The clustering algorithm used is hierarchical clustering. This novel clustering method of texts is applied on named entities and on the parts of speech of the words that compose the documents. As test data we are using the Reuters corpus of newspaper articles.
Starting Page	128
Ending Page	133
File Size	289036
Page Count	6
File Format	PDF
e-ISBN	9781479984817
DOI	10.1109/ICSTCC.2015.7321281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-14
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Heuristic algorithms Computational modeling Time series analysis Clustering algorithms Signal processing algorithms Time series Words Speech Natural language processing Clustering
Content Type	Text
Resource Type	Article

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