Semi-supervised document clustering via loci
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Document clustering is one of the prominent methods for mining important information from the vast amount of data available on the web. However, document clustering generally suffers from the curse of dimensionality. Providentially in high dimensional space, data points tend to be more concentrated in some areas of clusters. We take advantage of this phenomenon by introducing a novel concept of dynamic cluster representation named as loci. Clusters’ loci are efficiently calculated using documents’ ranking scores generated from a search engine. We propose a fast loci-based semi-supervised document clustering algorithm that uses clusters’ loci instead of conventional centroids for assigning documents to clusters. Empirical analysis on real-world datasets shows that the proposed method produces cluster solutions with promising quality and is substantially faster than several benchmarked centroid-based semi-supervised document clustering methods.
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|Item Type:||Journal Article|
|Additional Information:||Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1–3, 2015, Proceedings, Part II. Print ISBN 978-3-319-26186-7|
|Keywords:||Loci, Ranking, Semi-supervised clustering|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Past > Institutes > Institute for Creative Industries and Innovation
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 Springer International Publishing Switzerland|
|Copyright Statement:||The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-26187-4_16|
|Deposited On:||03 Nov 2015 03:44|
|Last Modified:||27 Nov 2015 11:24|
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