字符串 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 后的引号不完整。 ') and Issue_No=(select Issue_No from OA where Script_ID=@Script_ID) order by ID ' 附近有语法错误。 在线社交网络挖掘与搜索技术研究-《智能系统学报》

[1]石磊,杜军平,周亦鹏,等.在线社交网络挖掘与搜索技术研究[J].智能系统学报,2016,11(6):777-787.[doi:10.11992/tis.201612007]
 SHI Lei,DU Junping,ZHOU Yipeng,et al.A survey on online social network mining and search[J].CAAI Transactions on Intelligent Systems,2016,11(6):777-787.[doi:10.11992/tis.201612007]
点击复制

在线社交网络挖掘与搜索技术研究(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年6期
页码:
777-787
栏目:
出版日期:
2017-01-20

文章信息/Info

Title:
A survey on online social network mining and search
作者:
石磊1 杜军平1 周亦鹏2 叶杭1 赖金财1 何奕江1
1. 北京邮电大学 智能通信软件与多媒体北京市重点实验室, 北京 100876;
2. 北京工商大学 计算机与信息工程学院, 北京 100048
Author(s):
SHI Lei1 DU Junping1 ZHOU Yipeng2 YE Hang1 LAI Jincai1 HE Yijiang1
1. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. School of Computer Science and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
关键词:
社交网络数据挖掘搜索社区发现信息传播
Keywords:
social networksdata miningsearchcommunity detectioninformation transmission
分类号:
TP393
DOI:
10.11992/tis.201612007
摘要:
随着在线社交网络的蓬勃发展,传统的数据挖掘的和搜索方法已经不能完全适用于Web 2.0时代的社交网络。社交网络具有社交关系复杂、数据量大、动态更新、数据多模态等特点,给数据挖掘和搜索的研究来了巨大的挑战。因此,研究基于社交网络挖掘和搜索的新方法成为学术界和工业界的一项新任务。文章全面分析了社交网络发展的基本情况和存在的问题,阐述了社交网络结构建模、信息传播机制、社区发现、情感分析、事件监测及社交网络搜索排序技术的主要研究工作,并基于已有研究工作对社交网络挖掘和网络搜索技术进行了分析和展望。
Abstract:
With the vigorous development of online social networks, the traditional technologies of data mining and searching cannot solve the problems of social networks in the Web 2.0 era. Social networks, accompanied by complex social relationships, large amounts of data, dynamic updates, multimodal data, etc. have brought great challenge to the study of data mining and searching. Therefore, the research of novel algorithms of social network mining and searching has become a new task in both academia and industry. This paper summarized the basic situation and problems of social networks, and analyzed structural modeling techniques, information transmission mechanisms, community detection, sentiment analysis, event detection and search ranking techniques of social networks. Based on the analysis of previous researches, the prospect of social network data mining and search technologies was forecasted in this paper.

参考文献/References:

[1] 李立耀, 孙鲁敬, 杨家海. 社交网络研究综述[J]. 计算机科学, 2015, 42(11):8-21, 42. LI Liyao, SUN Lujing, YANG Jiahai. Research on online social network[J]. Computer science, 2015, 42(11):8-21, 42.
[2] 王大玲, 冯时, 张一飞, 等. 社会媒体多模态、多层次资源推荐技术研究[J]. 智能系统学报, 2014, 9(3):265-275. WANG Daling, FENG Shi, ZHANG Yifei, et al. Study on the recommendations of multi-modal and multi-level resources in social media[J]. CAAI transactions on intelligent systems, 2014, 9(3):265-275.
[3] AGRAWAL R, GOLSHAN B, PAPALEXAKIS E. Whither social networks for web search[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA:ACM, 2015:1661-1670.
[4] 贺超波, 汤庸, 麦辉强, 等. 在线社交网络挖掘综述[J]. 武汉大学学报:理学版, 2014, 60(3):189-200. HE Chaobo, TANG Yong, MAI Huiqiang, et al. A survey on online social network Mining[J]. Journal of Wuhan university:natural science edition, 2014, 60(3):189-200.
[5] SHENG Q Z, VASILAKOS A V, YU Qi, et al. Guest editorial:big data analytics and the web[J]. IEEE transactions on big data, 2015, 1(4):123-124.
[6] 唐杰, 陈文光. 面向大社交数据的深度分析与挖掘[J]. 科学通报, 2015, 60(5/6):509-519. TANG Jie, CHEN Wenguang. MAI Huiqiang Deep analytics and mining for big social data[J]. Chinese science bulletin, 2015, 60(5/6):509-519.
[7] 许进, 杨扬, 蒋飞, 等. 社交网络结构特性分析及建模研究进展[J]. 中国科学院院刊, 2015, 30(2):216-228. XU Jin, YANG Yang, JIANG Fei, et al. Social network structure feature analysis and its modelling[J]. Bulletin of Chinese academy of sciences, 2015, 30(2):216-228.
[8] AGGARWAL C C. Social network analysis[J]. Encyclopedia of social network analysis & mining, 2015, 22(1):109-127.
[9] HSU T Y, KSHEMKALYANI A D. Modeling social network topology with variable social vector clocks[C]//Proceedings of 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Paris, France:IEEE, 2015:584-589.
[10] DONG Yuxiao. User modeling in large social networks[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2016:713.
[11] SLAUGHTER A J, KOEHLY L M. Multilevel models for social networks:hierarchical bayesian approaches to exponential random graph modeling[J]. Social networks, 2016, 44:334-345.
[12] AMATO F, MOSCATO V, PICARIELLO A, et al. Multimedia social network modeling:a proposal[C]//Proceedings of 2016 IEEE Tenth International Conference on Semantic Computing. Laguna Hills, CA, USA:IEEE, 2016:448-453.
[13] BAJAJ A, SEN S. Simulating the effect of social network structure on workflow efficiency performance[J]. Social networking, 2014, 3(1):32-40.
[14] MIHOUB A, BAILLY G, WOLF C, et al. Graphical models for social behavior modeling in face-to face interaction[J]. Pattern recognition letters, 2016, 74:82-89.
[15] RODRIGUEZ M G, BALDUZZI D, SCHÖLKOPF B. Uncovering the temporal dynamics of diffusion Networks[C]//Proceedings of the 28th International Conference on Machine Learning. Bellevue, Washington, USA:ICML, 2011:561-568.
[16] JONES S, WEUTHEN T, HARMER Q J, et al. Modeling information propagation with survival theory[J]. Philosophical magazine letters, 2013, 95(2):85-91.
[17] RODRIGUEZ M G, LESKOVEC J, BALDUZZI D, et al. Uncovering the structure and temporal dynamics of information propagation[J]. Network science, 2014, 2(1):26-65.
[18] SADIKOV E, MEDINA M, LESKOVEC J, et al. Correcting for missing data in information cascades[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2011:55-64.
[19] ROMERO D M, GALUBA W, ASUR S, et al. Influence and passivity in social media[M]//Gunopulos D, Hofmann T, Hofmann D, et al. Machine Learning and Knowledge Discovery in Databases. Berlin Heidelberg:Springer, 2010:18-33.
[20] KIMURA M, SAITO K, OHARA K, et al. Speeding-up node influence computation for huge social networks[J]. International journal of data science and analytics, 2016, 1(1):3-16.
[21] GUILLE A, HACID H, FAVRE C. Predicting the temporal dynamics of information diffusion in social networks[J]. Computer science, 2013, 144(1):1145-1152.
[22] XU Xin, CHEN Xin, EUN D Y. Modeling time-sensitive information diffusion in online social networks[C]//Proceedings of 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Hong Kong, China:IEEE, 2015:408-413.
[23] WEN Sheng, HAGHIGHI M S, CHEN Chao, et al. A sword with two edges:propagation studies on both positive and negative information in online social networks[J]. IEEE transactions on computers, 2015, 64(3):640-653.
[24] TUAROB S, TUCKER C S, SALATHE M, et al. Modeling individual-level infection dynamics using social network information[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York, NY, USA:ACM, 2015:1501-1510.
[25] TAMBUSCIO M, RUFFO G, FLAMMINI A, et al. Fact-checking effect on viral hoaxes:a model of misinformation spread in social networks[C]//Proceedings of the 24th International Conference on World Wide Web. New York, NY, USA:ACM, 2015:977-982.
[26] WANG Ru, RHO S, CHEN Bowei, et al. Modeling of large-scale social network services based on mechanisms of information diffusion:Sina Weibo as a case study[J]. Future generation computer systems, 2016, doi:10.1016/j.future.2016.03.018.
[27] PAL A, COUNTS S. Identifying topical authorities in microblogs[C]//Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2011:45-54.
[28] SUO Qi, SUN Shiwei, HAJLI N, et al. User ratings analysis in social networks through a hypernetwork method[J]. Expert systems with applications, 2015, 42(21):7317-7325.
[29] 吴岘辉, 张晖, 赵旭剑, 等. 基于用户行为网络的微博意见领袖挖掘算法[J]. 计算机应用研究, 2015, 32(9):2678-2683. WU Xianhui, ZHANG Hui, ZHAO Xujian, et al. Mining algorithm of microblogging opinion leaders based on user-behavior network[J]. Application research of computers, 2015, 32(9):2678-2683.
[30] SUPPA P, ZIMEO E. A clustered approach for fast computation of betweenness centrality in social networks[C]//Proceedings of 2015 IEEE International Congress on Big Data. New York, NY, USA:IEEE, 2015:47-54.
[31] YANG Yang, TANG Jie, LEUNG C W K, et al. Rain:social role-aware information diffusion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin, Texas, USA:AAAI, 2015:367-373.
[32] SUBBIAN K, AGGARWAL C C, SRIVASTAVA J. Querying and tracking influencers in social streams[C]//Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2016:493-502.
[33] SU Jianhai, HAVENS T C. Quadratic program-based modularity maximization for fuzzy community detection in social networks[J]. IEEE transactions on fuzzy systems, 2015, 23(5):1356-1371.
[34] KLOSTER K, GLEICH D F. Heat kernel based community detection[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA:ACM, 2014:1386-1395.
[35] ALTUNBEY F, ALATAS B. Overlapping community detection in social networks using parliamentary optimization algorithm[J]. International journal of computer networks and applications, 2015, 2(1):12-19.
[36] ARAB M, AFSHARCHI M. Community detection in social networks using hybrid merging of sub-communities[J]. Journal of network and computer applications, 2014, 40:73-84.
[37] CHEN Pinyu, HERO A O. Deep community detection[J]. IEEE transactions on signal processing, 2015, 63(21):5706-5719.
[38] ZHANG Yuan, LEVINA E, ZHU Ji. Detecting overlapping communities in networks using spectral methods[J]. Physica a:statistical mechanics and its applications, 2014, 405:1-37.
[39] GAO Chao, MA Zongming, ZHANG A Y, et al. Achieving optimal misclassification proportion in stochastic block model[J]. Computer science, 2015, 20(3):88-90.
[40] MAHMOOD A, SMALL M. Subspace based network community detection using sparse linear coding[J]. IEEE transactions on knowledge and data engineering, 2016, 28(3):801-812.
[41] AIROLDI E M, BLEI D M, FIENBERG S E, et al. Mixed membership stochastic blockmodels[J]. The journal of machine learning research, 2008, 9:1981-2014.
[42] 赵文清, 侯小可, 沙海虹. 语义规则在微博热点话题情感分析中的应用[J]. 智能系统学报, 2014, 9(1):121-125. ZHAO Wenqing, HOU Xiaoke, SHA Haihong. Application of semantic rules to sentiment analysis of microblog hot topics[J]. CAAI Transactions on intelligent systems, 2014, 9(1):121-125.
[43] BRAVO-MARQUEZ F, MENDOZA M, POBLETE B. Combining strengths, emotions and polarities for boosting Twitter sentiment analysis[C]//Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining. New York, NY, USA:ACM, 2013:2.
[44] HU Xia, TANG Lei, TANG Jiliang, et al. Exploiting social relations for sentiment analysis in microblogging[C]//Proceedings of the Sixth ACM International Conference on Web Search and Data Mining. New York, NY, USA:ACM, 2013:537-546.
[45] NASKAR D, MOKADDEM S, REBOLLO M, et al. Sentiment analysis in social networks through topic modeling[C]//Proceedings of the 10th Edition of the Language Resources and Evaluation Conference (LREC) 2016. Portoroz:LREC, 2016.
[46] SIXTO J, ALMEIDA A, LÓPEZ-DE-IPIÑA D. Improving the sentiment analysis process of spanish tweets with bm25[M]//MÉTAIS E, MEZIANE F, SARAEE M, et al. Natural Language Processing and Information Systems. Switzerland:Springer, 2016:285-291.
[47] YOU Quanzeng, LUO Jiebo, JIN Hailin, et al. Robust image sentiment analysis using progressively trained and domain transferred deep networks[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Austin, Texas, USA:AAAI Press, 2015:381-388.
[48] CHAO Linlin, TAO Jianhua, YANG Minghao, et al. Long short term memory recurrent neural network based multimodal dimensional emotion recognition[C]//Proceedings of the 5th International Workshop on Audio/Visual Emotion Challenge. New York, NY, USA:ACM, 2015:65-72.
[49] PORIA S, CAMBRIA E, HOWARD N, et al. Fusing audio, visual and textual clues for sentiment analysis from multimodal content[J]. Neurocomputing, 2016, 174:50-59.
[50] KALEEL S B, ABHARI A. Cluster-discovery of twitter messages for event detection and trending[J]. Journal of computational science, 2015, 6:47-57.
[51] D’ANDREA E, DUCANGE P, LAZZERINI B, et al. Real-time detection of traffic from twitter stream analysis[J]. IEEE transactions on intelligent transportation systems, 2015, 16(4):2269-2283.
[52] LI Jianxin, WEN Jianfeng, TAI Zhenying, et al. Bursty event detection from microblog:a distributed and incremental approach[J]. Concurrency and computation practice and experience, 2016, 28(11):3115-3130.
[53] ZHANG Xiaoming, CHEN Xiaoming, CHEN Yan, et al. Event detection and popularity prediction in microblogging[J]. Neurocomputing, 2015, 149:1469-1480.
[54] ZHOU Xiangmin, CHEN Lei. Event detection over twitter social media streams[J]. The VLDB journal, 2014, 23(3):381-400.
[55] POHL D, BOUCHACHIA A, HELLWAGNER H. Social media for crisis management:clustering approaches for sub-event detection[J]. Multimedia tools and applications, 2015, 74(11):3901-3932.
[56] GUILLE A, FAVRE C. Mention-anomaly-based event detection and tracking in twitter[C]//Proceedings of 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Beijing, China:IEEE, 2014:375-382.
[57] ZHANG Yu, QU Zhiyi. A novel method for online bursty event detection on twitter[C]//Proceedings of the 20156th IEEE International Conference on Software Engineering and Service Science (ICSESS). Beijing, China:IEEE, 2015:284-288.
[58] YAN Yan, YANG Yi, MENG Deyu, et al. Event oriented dictionary learning for complex event detection[J]. IEEE transactions on image processing, 2015, 24(6):1867-1878.
[59] ABDELHAQ H, SENGSTOCK C, GERTZ M. Eventweet:online localized event detection from twitter[J]. Proceedings of the VLDB endowment, 2013, 6(12):1326-1329.
[60] SCHINAS M, PAPADOPOULOS S, PETKOS G, et al. Multimodal event detection and summarization in large scale image collections[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. New York, NY, USA:ACM, 2016:421-422.
[61] GAO Yue, ZHAO Sicheng, YANG Yang, et al. Multimedia social event detection in microblog[M]//HE Xiangjian, LUO Suhuai, TAO Dacheng, et al. MultiMedia Modeling. Switzerland:Springer International Publishing, 2015:269-281.
[62] UNANKARD S, LI Xue, SHARAF M A. Emerging event detection in social networks with location sensitivity[J]. World wide web, 2015, 18(5):1393-1417.
[63] BOUADJENEK M R, HACID H, BOUZEGHOUB M. Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms[J]. Information systems, 2016, 56:1-18.
[64] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3):582-600. LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of computer research and development, 2016, 53(3):582-600.
[65] 费洪晓, 莫天池, 秦启飞, 等. 社交网络相关机制应用于搜索引擎的研究综述[J]. 计算技术与自动化, 2014, 33(1):1-9. FEI Hongxiao, MO Tianchi, QIN Qifei, et al. The researches of applying social networking mechanism to search engine:a survey[J]. Computing technology and automation, 2014, 33(1):1-9.
[66] CHEN Chun, LI Feng, OOI B C, et al. Ti:an efficient indexing mechanism for real-time search on tweets[C]//Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. New York, NY, USA:ACM, 2011:649-660.
[67] CHEN Hanhua, JIN Hai. Efficient keyword searching in large-scale social network service[J]. IEEE transactions on services computing, 2015, doi:10.1109/TSC.2015.2464819.
[68] LI Yuchen, BAO Zhifeng, LI Guoliang, et al. Real time personalized search on social networks[C]//Proceedings of the 2015 IEEE 31st International Conference on Data Engineering. Seoul, South Korea:IEEE, 2015:639-650.
[69] ZHAO Feng, LIU Jun, ZHOU Jingyu, et al. LS-AMS:an adaptive indexing structure for realtime search on microblogs[J]. IEEE transactions on big data, 2015, 1(4):125-137.
[70] HUANG Haifei, LI Jianxin, ZHANG Richong, et al. Liveindex:a distributed online index system for temporal microblog data[C]//Proceedings of 2015 IEEE 17th International Conference on High Performance Computing and Communications (HPCC), the 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS). New York, NY, USA:IEEE, 2015:884-887.
[71] YUAN Jingbo, WANG Bairong, DING Shunli. A real-time search structure and classification algorithm of microblog based on partial indexing[J]. Indonesian journal of electrical engineering and computer science, 2014, 12(3):2271-2277.
[72] RÍSSOLA E A, TOLOSA G H. Inverted index entry invalidation strategy for real time search[C]//Proceedings of XXI Congreso Argentino de Ciencias de la Computación. Junín:CACIC, 2015.
[73] XIE Haoran, LI Xiaodong, WANG Tao, et al. Personalized search for social media via dominating verbal context[J]. Neurocomputing, 2016, 172:27-37.
[74] LIANG Shangsong, REN Zhaochun, WEERKAMP W, et al. Time-aware rank aggregation for microblog search[C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York, NY, USA:ACM, 2014:989-998.
[75] WANG Wenbo, DUAN Lei, KOUL A, et al. YouRank:let user engagement rank microblog search results[C]//Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media. Palo Alto, California:AAAI, 2014.
[76] LIU Lijun. Research on real-time personalized recommendation algorithm[J]. International journal of u-and e-service, science and technology, 2014, 7(5):359-368.
[77] 卫冰洁, 王斌. 面向微博搜索的时间感知的混合语言模型[J]. 计算机学报, 2014, 37(1):229-239. WEI Bingjie, WANG Bin. Time-aware mixed language model for microblog search[J]. Chinese journal of computers, 2014, 37(1):229-239.
[78] 周霞娟, 汪飞, 金玲, 等. 用户驱动的微博可视化搜索[J]. 中国图象图形学报, 2015, 20(5):715-723. ZHOU Xiajuan, WANG Fei, JIN Ling, et al. User-driven visual micro-blog search[J]. Journal of image and graphics, 2015, 20(5):715-723.
[79] SEVERYN A, MOSCHITTI A. Learning to rank short text pairs with convolutional deep neural networks[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA:ACM, 2015:373-382.
[80] CHY A N, ULLAH M Z, AONO M. Combining temporal and content aware features for microblog retrieval[C]//Proceedings of the 20152nd International Conference on Advanced Informatics:Concepts, Theory and Applications (ICAICTA). Chonburi, Thailand:IEEE, 2015:1-6.

相似文献/References:

[1]张继福,张素兰,胡立华.约束概念格及其构造方法[J].智能系统学报,2006,1(02):31.
 ZHANG Ji-fu,ZHANG Su-lan,HU Li-hua.Constrained concept lattice and its construction method[J].CAAI Transactions on Intelligent Systems,2006,1(6):31.
[2]王国胤,张清华,胡 军.粒计算研究综述[J].智能系统学报,2007,2(06):8.
 WANG Guo-yin,ZHANG Qing-hua,HU Jun.An overview of granular computing[J].CAAI Transactions on Intelligent Systems,2007,2(6):8.
[3]何清.物联网与数据挖掘云服务[J].智能系统学报,2012,7(03):189.
 HE Qing.The Internet of things and the data mining cloud service[J].CAAI Transactions on Intelligent Systems,2012,7(6):189.
[4]孔庆超,毛文吉,张育浩.社交网站中用户评论行为预测[J].智能系统学报,2015,10(03):349.[doi:10.3969/j.issn.1673-4785.201403019]
 KONG Qingchao,MAO Wenji,ZHANG Yuhao.User comment behavior prediction in social networking sites[J].CAAI Transactions on Intelligent Systems,2015,10(6):349.[doi:10.3969/j.issn.1673-4785.201403019]
[5]李海林,郭韧,万校基.基于特征矩阵的多元时间序列最小距离度量方法[J].智能系统学报,2015,10(03):442.[doi:10.3969/j.issn.1673-4785.201405047]
 LI Hailin,GUO Ren,WAN Xiaoji.A minimum distance measurement method for amultivariate time series based on the feature matrix[J].CAAI Transactions on Intelligent Systems,2015,10(6):442.[doi:10.3969/j.issn.1673-4785.201405047]
[6]申彦,朱玉全.CMP上基于数据集划分的K-means多核优化算法[J].智能系统学报,2015,10(04):607.[doi:10.3969/j.issn.1673-4785.201411036]
 SHEN Yan,ZHU Yuquan.An optimized algorithm of K-means based on data set partition on CMP systems[J].CAAI Transactions on Intelligent Systems,2015,10(6):607.[doi:10.3969/j.issn.1673-4785.201411036]
[7]王景丽,许立波,庞超逸.复杂网络中的在线社交网络演化模型[J].智能系统学报,2015,10(6):949.[doi:10.11992/tis.201507042]
 WANG Jingli,XU Libo,PANG Chaoyi.Evolution model of online social networks based on complex networks[J].CAAI Transactions on Intelligent Systems,2015,10(6):949.[doi:10.11992/tis.201507042]
[8]汤建国,汪江桦,韩莉英,等.基于覆盖粗糙集的语言动力系统[J].智能系统学报,2014,9(02):229.[doi:10.3969/j.issn.1673-4785.201307018]
 TANG Jianguo,WANG Jianghua,HAN Liying,et al.Linguistic dynamic systems based on covering-based rough sets[J].CAAI Transactions on Intelligent Systems,2014,9(6):229.[doi:10.3969/j.issn.1673-4785.201307018]
[9]淦文燕,刘冲.一种改进的搜索密度峰值的聚类算法[J].智能系统学报,2017,12(02):229.[doi:10.11992/tis.201512036]
 GAN Wenyan,LIU Chong.An improved clustering algorithm that searches and finds density peaks[J].CAAI Transactions on Intelligent Systems,2017,12(6):229.[doi:10.11992/tis.201512036]
[10]翟俊海,刘博,张素芳.基于粗糙集相对分类信息熵和粒子群优化的特征选择方法[J].智能系统学报,2017,12(03):397.[doi:10.11992/tis.201705004]
 ZHAI Junhai,LIU Bo,ZHANG Sufang.A feature selection approach based on rough set relative classification information entropy and particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2017,12(6):397.[doi:10.11992/tis.201705004]

备注/Memo

备注/Memo:
收稿日期:2016-12-06。
基金项目:国家自然科学基金重点项目(61532006);国家自然科学基金重大国际合作项目(61320106006).
作者简介:石磊,男,1986年生,博士研究生,主要研究方向为人工智能和数据挖掘;杜军平,女,1963年生,教授,博士生导师,博士,中国人工智能学会常务理事,智能服务专业委员会主任,主要研究方向为社交网络分析、数据挖掘、运动图像处理,主持国家"863"、"973"计划项目、国家自然科学基金重点项目、国家自然科学基金重大国际合作项目、北京市自然科学基金重点项目等多项;周亦鹏,男,副教授,博士,主要研究方向为Web挖掘、跨媒体信息检索。
通讯作者:杜军平.E-mail:junpingdu@126.com.
更新日期/Last Update: 1900-01-01