Please use this identifier to cite or link to this item:
Title: Topic-level clustering on web resources
Author(s): Wang, Philips Fu Lee 
Wong, Leung Pun 
Author(s): Zhao, S.
Issue Date: 2017
Publisher: Springer
Related Publication(s): Emerging Technologies for Education (First International Symposium, SETE 2016) Revised Selected Papers
Start page: 564
End page: 573
The rapid development of Internet, social media, and news portals has provided a large amount of information in various aspects. Confronting such plenty of resources, it is valuable to develop effective clustering approaches. However, performance of traditional clustering models on web resources is not good enough due to the high dimension. In this paper, we propose a clustering model based on topic model and density peaks. Our model combines biterm topic model and clustering by fast search of density peaks, which firstly extract a set of features with the co-occurrence of two words from the original documents, followed by clustering analysis via topical features. Web resources are translated from raw data into clusters, and evaluation on clustering results of center part verifies the effectiveness of the proposed method.
DOI: 10.1007/978-3-319-52836-6_60
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

SFX Query Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.