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Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Semantic Overlapping Clustering Algorithm for Analyzing Short-Texts

Authors : Lipika Dey, Kunal Ranjan, Ishan Verma, Abir Naskar

Published in: Rough Sets

Publisher: Springer International Publishing

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Abstract

The rise in volumes of digitized short-texts like tweets or customer complaints and opinions about products and services pose new challenges to the established methods of text analytics both due to the sparseness of text and noise. In this paper we present a new semantic clustering algorithm, which first discovers frequently occurring semantic concepts within a repository, and then clusters the documents around these concepts based on concept distribution within them. The method produces overlapping clusters which generates far more accurate view of content embedded within real-life communication texts. We have compared the clustering results with LSH based clustering and show that the proposed method produces fewer overall clusters with more semantic coherence within a cluster.

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previous chapter Learning Latent Features for Multi-view Clustering Based on NMF
next chapter On Behavior of Statistical Indices in Incremental Context
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Metadata
Title
A Semantic Overlapping Clustering Algorithm for Analyzing Short-Texts
Authors
Lipika Dey
Kunal Ranjan
Ishan Verma
Abir Naskar
Copyright Year
2016
Book
Rough Sets

Print ISBN: 978-3-319-47159-4

Electronic ISBN: 978-3-319-47160-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47160-0_43

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