NDLI: An Improved K-means Algorithm for Document Clustering

Content Provider	IEEE Xplore Digital Library
Author	Guohua Wu Hairong Lin Ershuai Fu Liuyang Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Hang Zhou Dian Zi Univ., Hangzhou, China (Guohua Wu; Hairong Lin; Ershuai Fu; Liuyang Wang)
Abstract	K-Means algorithm has a major shortcoming of high dimensional and sparse data. So the traditional measurement of the distance can't deal with the data effectively. Motivated by this, this paper proposed a K-Means algorithm based on Sim Hash. After preprocessing of the text, Sim Hash is used to calculate the feature vectors extracted, and then the fingerprint of each text is obtained. Sim Hash not only reduces the dimension of the text, but also directly calculates the Hamming distance between the fingerprints as the vector distance. According to the Hamming distance, it can judge which clustering the data is belongs to. Experimental result shows that the algorithm guarantees the quality of the clustering, and greatly reduces the speed of K-means clustering algorithm.
Starting Page	65
Ending Page	69
File Size	158758
Page Count	5
File Format	PDF
e-ISBN	9781467391665
DOI	10.1109/CSMA.2015.20
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer science SimHash Hamming distance K-Means Clustering algorithms Fingerprint recognition Feature extraction Text clustering Classification algorithms
Content Type	Text
Resource Type	Article

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