Advanced Overlap Community Detection by Associative Rule Mining and Multi-View Ant Clustering
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https://doi.org/10.14419/ijet.v7i3.34.19588
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Clustering, Association Rule, Data Mining, Recommendation system. -
Abstract
Recommendation systems mean based on customers interest techniques and tools are to generate the new products and services. The main issue in this recommendation system is the number of users are more and giving preference to their items takes more time. And also processing the date takes more time. Hence, clustering techniques are used for users into overlapping groups helps in the information sparsely issue and improve recommendation range. Next essential factor in this system is dynamic attention on users in which their importance varies. This work mainly concentrates on using the ant clustering technique to improvethe multi-view clustering method.
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References
[1] Vassillios statho, Ioannis Konstas poulos and Joemon M.Jose, “On social networks and collaborative recommendationâ€, SIGIR ‘09 Proceedings of the 32nd international ACM SIGIR conference, USA ©2009.
[2] P. Resnick and H. R. Varian, “Recommender systems and its methodsâ€,Communications of the ACM, vol. 40, pp. 56–58, March 1997.
[3] Bahram Amini, Roliana Ibrahim and Mohd Shahizan Othman, “Discovering the impact of knowledge in recommender systemsâ€, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 2011.
[4] Giancarlo Ruffo and Rossano Schifanella, “Evaluating Peer-to-Peer Recommender Systems that Exploit Spontaneous Affinitiesâ€, ACM symposium on Applied computing, Pages 1574-1578, USA, 2007.
[5] Shaghayegh Sahebi and William W. Cohen, “Community-Based Recommendations: a Solution to the Cold Start Problemâ€, workshop on Recommender systems and the social Web(RSWEB),ACM RecSys’11,Chicago,23 October,2011.
[6] Xiaofang Ding, Zhixiao Wang*, Shaoda Chen and Ying Huang,â€Community-based Collaborative Filtering Recommendation Algorithmâ€,International Journal of Hybrid Information Technology, Vol.8, pp.149-158, No.2 (2015).
[7] Brian Lee Yung Rowe ,Zato Novo, “Using social network graph analysis for interest Detectionâ€, International journal of Data Analytics, School of Professional Studies, CUNY,October 2, 2014.
[8] Maryam Fatemi, Laurissa Tokarchuk, “A Community Based Social recommender System for Individuals & Groupsâ€, 978-0-7695-5137-1/2013 IEEE,DOI 10.1109/SocialCom.2013.
[9] Isabel M. Kloumann,â€Community membership identification from small seed setsâ€,20thACM international conference on Knowledge discovery and data mining, Pages 1366-1375, New York, USA, 2014.
[10] Badrul M. Sarwar, George Karypis, Joseph Konstan, and John Riedl,“Recommender Systems for Large scale E-Commerce: Scalable Neighborhood Formation Using Clusteringâ€, GroupLens Research Group, Minneapolis, MN 55455, USA, 2004.
[11] K. Vijayakumar and C. Arun, “Continuous Security Assessment of Applications in Cloud Environmentâ€, International Journal of Control Theory and Applications, ISSN: 0974-5645 volume No. 9(36), Sep 2016, Page No. 533-541
[12] K. Vijayakumar C. Arun, Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC, Cluster Computing DOI 10.1007/s10586-017-1176-x, Sept 2017.
[13] Purushottam Sharma and Kanak Saxena, “Temporal Weighted Association Rule Mining for Classificationâ€, International Journal of Computer Theory and Engineering, Vol. 4, No. 5, October 2012.[14] C.-H. Lee, Y.-H. Kim, P.-K. Rhee,Web personalization expert with combining collaborative filtering and association rule mining techniqueâ€, ACM Conference onExpert Systems, Volume 21, Issue 3, October 2001, Pages 131–137.[15] Urszula Boryczka, “Ant Clustering Algorithmâ€, Intelligent Information Systems, ISBN 978-83-60434-44-4, pages 377–386, 2008.
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How to Cite
P.Latchoumi, T., Parthiba, L., P.Ezhilarasi, T., Dilip, G., & Bhargavi, H. (2018). Advanced Overlap Community Detection by Associative Rule Mining and Multi-View Ant Clustering. International Journal of Engineering & Technology, 7(3.34), 913-917. https://doi.org/10.14419/ijet.v7i3.34.19588Received date: 2018-09-12
Accepted date: 2018-09-12