[1]刘璐,贾彩燕.基于文本扩展模型的网络视频聚类方法[J].智能系统学报,2017,12(6):799-805.[doi:10.11992/tis.201706036]
 LIU Lu,JIA Caiyan.Web video clustering method based on an extended text model[J].CAAI Transactions on Intelligent Systems,2017,12(6):799-805.[doi:10.11992/tis.201706036]
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基于文本扩展模型的网络视频聚类方法

参考文献/References:
[1] WU X, ZHAO W L, NGO C W. Towards google challenge: combining contextual and social information for web video categorization[C]//International Conference on Multimedia 2009. Vancouver, Canada, 2009: 1109-1110.
[2] YANG L, LIU J, YANG X, et al. Multi-modality web video categorization[C]//ACM Sigmm International Workshop on Multimedia Information Retrieval. Augsburg, Germany, 2007: 265-274.
[3] HINDLE A, SHAO J, LIN D, et al. Clustering Web video search results based on integration of multiple features[J]. World wide web, 2011, 14(1): 53-73.
[4] NGUYEN P Q, NGUYEN-THI A T, NGO T D, et al. Using textual semantic similarity to improve clustering quality of web video search results[C]//2015 IEEE Seventh International Conference on Knowledge and Systems Engineering (KSE). Ho Chi Minh, Vietnam, 2015: 156-161.
[5] LIU S, ZHU M, ZHENG Q. Mining similarities for clustering web video clips[C]//International Conference on Computer Science and Software Engineering. Wuhan, China, 2008: 759-762.
[6] KAMIE M, HASHIMOTO T, KITAGAWA H. Effective web video clustering using playlist information[C]//Proceedings of the 27th Annual ACM Symposium on Applied Computing. Trento, Italy, 2012: 949-956.
[7] HUANG H, LU Y, ZHANG F, et al. A multi-modal clustering method for web videos[J]. Communications in computer and information science, 2013, 320: 163-169.
[8] ZHANG D Q, LIN C Y, CHANG S F, et al. Semantic video clustering across sources using bipartite spectral clustering [C]//IEEE International Conference on Multimedia and Expo. Taipei, China, 2004: 117-120.
[9] ZHANG J R, SONG Y, LEUNG T. Improving video classification via youtube video co-watch data[C]//Proceedings of the 2011 ACM Workshop on Social and Behavioural Networked Media Access. Scottsdale, USA, 2011: 21-26.
[10] YIN J, WANG J. A dirichlet multinomial mixture model-based approach for short text clustering[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014: 233-242.
[11] YAN X, GUO J, LIU S, et al. Learning topics in short texts by non-negative matrix factorization on term correlation matrix[C]//Proceedings of the 2013 SIAM International Conference on Data Mining. Austin, USA, 2013: 749-757.
[12] SAHAMI M, HEILMAN T D. A Web-based kernel function for measuring the similarity of short text snippets[C]//International Conference on World Wide Web, WWW 2006. Edinburgh, Scotland, UK, 2006: 377-386.
[13] YIH W, MEEK C. Improving similarity measures for short segments of text[J]. Proceedings of artificial intelligence, Pune, India, 2007: 1489-1494.
[14] BANERJEE S, RAMANATHAN K, GUPTA A. Clustering short texts using wikipedia[C]//SIGIR 2007: Proceedings of the, International ACM SIGIR Conference on Research and Development in Information Retrieval. Amsterdam, the Netherlands, 2007: 787-788.
[15] GABRILOVICH E, MARKOVITCH S. Feature generation for text categorization using world knowledge[C]//International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers Inc, 2005: 1048-1053.
[16] HU X, SUN N, ZHANG C, et al. Exploiting internal and external semantics for the clustering of short texts using world knowledge[C]//ACM Conference on Information and Knowledge Management 2009. Hong Kong, China, 2009: 919-928.
[17] HOTHO A, STAAB S, STUMME G. Wordnet improves text document clustering[C]//Proceedings of Semantic Web Workshop, the 26th annual International ACM SIGIR Conference. Toronto, Canada, 2003: 541-544.
[18] SONG Y, WANG H, WANG Z, et al. Short text conceptualization using a probabilistic knowledgebase[C]//Proceedings of the, International Joint Conference on Artificial Intelligence. Barcelona, Spain, 2011: 2330-2336.
[19] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3: 993-1022.
[20] YANG L, QIU M, GOTTIPATI S, et al. CQArank: jointly model topics and expertise in community question answering[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. San Francisco, USA, 2013:99-108.
[21] ARTHUR D, VASSILVITSKⅡ S. k-means++:the advantages of careful seeding[C]//Eighteenth Acm-Siam Symposium on Discrete Algorithms 2007. New Orleans, USA, 2007: 1027-1035.
[22] CAI D, HE X, HAN J, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 33(8): 1548-1560.
[23] YAN X, GUO J, LAN Y, et al. A biterm topic model for short texts[C]//International Conference on World Wide Web. Rio, Brazil, 2013: 1445-1456.

备注/Memo

收稿日期:2017-06-09;改回日期:。
基金项目:国家自然科学基金项目(61473030).
作者简介:刘璐,女,1994年生,硕士研究生,主要研究方向为数据挖掘、文本聚类;贾彩燕,女,1976年生,教授,博士生导师,博士,中国人工智能学会“粗糙集与软计算专业委员会”委员,主要研究方向为数据挖掘、社会计算、生物信息学。发表学术论文50余篇。
通讯作者:贾彩燕.E-mail:cyjia@bjtu.edu.cn.

更新日期/Last Update: 2018-01-03
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