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An improved clustering algorithm for text mining : multi-cluster spherical K-means
Joint Authors
Tunali, Volkan
Bilgin, Turgay
Camurcu, Ali
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 1 (31 Jan. 2016)8 p.
Publisher
Publication Date
2016-01-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Thanks to advances in information and communication technologies, there is a prominent increase in the amount of information produced specifically in the form of text documents.
In order to, effectively deal with this “information explosion” problem and utilize the huge amount of text databases, efficient and scalable tools and techniques are indispensable.
In this study, text clustering which is one of the most important techniques of text mining that aims at extracting useful information by processing data in textual form is addressed.
An improved variant of Spherical K-means algorithm named multi-cluster spherical K-means is developed for clustering high dimensional document collections with high performance and efficiency.
Experiments were performed on several document data sets and it is shown that the new algorithm provides significant increase in clustering quality without causing considerable difference in CPU time usage when compared to Spherical Kmeans algorithm.
American Psychological Association (APA)
Tunali, Volkan& Bilgin, Turgay& Camurcu, Ali. 2016. An improved clustering algorithm for text mining : multi-cluster spherical K-means. The International Arab Journal of Information Technology،Vol. 13, no. 1.
https://search.emarefa.net/detail/BIM-581156
Modern Language Association (MLA)
Tunali, Volkan…[et al.]. An improved clustering algorithm for text mining : multi-cluster spherical K-means. The International Arab Journal of Information Technology Vol. 13, no. 1 (Jan. 2016).
https://search.emarefa.net/detail/BIM-581156
American Medical Association (AMA)
Tunali, Volkan& Bilgin, Turgay& Camurcu, Ali. An improved clustering algorithm for text mining : multi-cluster spherical K-means. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 1.
https://search.emarefa.net/detail/BIM-581156
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-581156