@inproceedings{e9cb6ae9192143c3a01719e7a05f83ac,
title = "Topic-level clustering on web resources",
abstract = "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.",
keywords = "Biterm, Density peaks, Document clustering, Topic model",
author = "Shiyu Zhao and Wang, {Fu Lee} and Wong, {Leung Pun}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 1st International Symposium on Emerging Technologies for Education, SETE 2016 Held in Conjunction with ICWL 2016 ; Conference date: 26-10-2016 Through 29-10-2016",
year = "2017",
doi = "10.1007/978-3-319-52836-6_60",
language = "English",
isbn = "9783319528359",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "564--573",
editor = "Rosella Gennari and Yiwei Cao and Yueh-Min Huang and Wu Wu and Haoran Xie",
booktitle = "Emerging Technologies for Education - 1st International Symposium, SETE 2016 Held in Conjunction with ICWL 2016, Revised Selected Papers",
}