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New Technology of Library and Information Service  2010, Vol. 26 Issue (4): 66-71    DOI: 10.11925/infotech.1003-3513.2010.04.11
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Research on the Hot Topics Discovery Algorithm Based on Improved Ant Colony Clustering
 Lu  Bei, Cheng  Xiao, Chen  Zhi-Qun
(Institute of Computer Application Technology, Hangzhou Dianzi University, Hangzhou 310018, China)
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For the hot topics found is based on the clustering algorithm,this paper introduces the improved ant colony clustering algorithm,and raises Class Attention Degree (CAD) concept in order to determine the class of hot level and to distinguish popular categories as well as unpopular categories. Meanwhile,hot topic set is erxtracted on this basis. Experimental results show that the improved ant colony clustering algorithm has in certain effects to the hot topics found.

Key wordsOnline public opinion        Ant-clustering        Hot categories        Topic     
Received: 22 March 2010      Published: 25 April 2010


Corresponding Authors: Cheng Xiao     E-mail:

Cite this article:

Lu Bei, Cheng Xiao, Chen Zhi-Qun. Research on the Hot Topics Discovery Algorithm Based on Improved Ant Colony Clustering. New Technology of Library and Information Service, 2010, 26(4): 66-71.

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[1] 第24次中国互联网络发展状况调查统计报告[EB/OL]. (2009-07-16).[2010-02-22].
[2] 陆蓓,程肖,谌志群.互联网舆情挖掘研究综述[J].情报资料工作,2010(2):41-45.
[3] Allan J. Topic Detection and Tracking:Event-based Information Organization [M].Kluwer Academic Publishers,2002.
[4] Lumer E D, Faieta B. Diversity and Adaptation in Populations of Clustering Ants[C].In:Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior:From Animals to Animals. 1994:501- 508.
[5] 鄢文晋.蚁群算法及其在数据挖掘中的应用[D].重庆:重庆大学,2007.
[6] 曾海群.蚁群聚类算法研究[D].长沙:中南大学,2008.
[7] 莫锦萍,陈琴,马琳,等.一种新的K-Means蚁群聚类算法[J].广西社会科学院学报,2008,24(4):102-104.
[8] 段海滨.蚁群算法原理及其应用[M].北京:科学出版社,2007:290-297.
[9] 吴斌,郑毅,傅伟鹏,等. 一种基于群体智能的客户行为分析算法[J].计算机学报, 2003, 26(8):913-918.
[10] 李保利,俞士汶.话题识别与跟踪研究[J].计算机工程与应用,2003,39(17):7-10.
[11] 罗亚平,王枞,周延泉.基于关注度的热点话题发现模型[C].见:第七届中文信息处理国际会议论文集.武汉:中国中文信息学会,2007:402-408.
[12] 曾依灵,许洪波.网络热点信息发现研究[J].通信学报,2007,28(12):141-146.
[13] 刘星星,何婷婷,龚海军,等.网络热点事件发现系统的设计[J].中文信息学报,2008,22(6):80-85.

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