Cloud-Enabled Social Network Mining for Advanced Recommendation Systems: An Integrated Data Mining and Social Network Analysis Approach
Keywords:
Cloud Computing, Social Network, Data mining, Recommendation System, Normalization.Abstract
Recommendation systems play a crucial role in assisting users in discovering relevant and personalized content in social networks. For recommendation system data mining has a profound impact on several domains, including databases, artificial intelligence, machine learning, and social networks. It plays a crucial role in driving significant research advancements in the field. In today's fast-paced world, where data is rapidly expanding and information retrieval poses complex challenges, users increasingly demand valuable insights from their vast datasets. Social networks have emerged as a fascinating domain that has made substantial contributions to data mining research, ushering in a new era of possibilities. To determine the intrusion index based on the source address of the network security alarm, a simulation test is run. The findings demonstrate that this strategy can successfully implement cloud network security situation awareness as the related window attack index drops as soon as the security event is cancelled. You can accurately detect changes in network security circumstances using the suggested technique
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References
Loyalty Square: Social Network Analysis https://www.indiamart.com/loyaltysquare/aboutus.html”.
Liben-Nowell, D., Kleinberg, J.: “The link prediction problem for social Networks”: Twelfth international conference on Information and Knowledge Management, ACM Press, New York, pp. 556-559(2003).
Pavlov, M., Ichise, R.: “Finding experts by link prediction in co-authorship networks”: 2nd International Workshop on Finding Experts on the Web with Semantics, pp. 42-55(2007).
Mrinmaya Sachan, Ryutaro Ichise. “Using Abstract information and Community Alignment Information for Link Prediction”: Second International Conference on Machine Learning and Computing (ICMLC), ISBN: 978-1-4244- 6006-9, pp. 61-65(2009).
Jingfeng Guo, hongwei Guo.”Mullti-features Link Prediction Based on Matrix”: International Conference on Computer Design and Applications (ICCDA) ISBN: 978-1-4244-7164-5, V1-357 - V1-361(2010).
Victor Stroele, Jonice Oliveria et. Al.”Mining and Analyzing Multi Relational Social Networks”: International Conference on Computational Science and Engineering, Print ISBN: 978- 1-4244-5334-4, pp. 711-716(2009).
Getoor, L., Diehl, C.P.: “Link Mining: A Survey “: ACM-SIGKDD Explorations, Volume 7, Issue 2(2005).
Evan Wei Xiang, Qiang Yang.: “A Survey on Link prediction Model for Social Network Data”. Science and Technology (2008).
Tarun Kumar, O. P. Vyas.: “Harnessing Social Network with link Data Mining for Predictive Analytics: An Extended Approach”: 2nd IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence (ICADABAI) (2011).
Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütze.: “Introduction to Information Retrieval”, Cambridge University Press (2008).
Pavan, Rytaro Ichise, O.P. Vays.:”LiDDM: A Data Mining System for Linked Data”:WWW 2011 workshop: Linked Data on the web (LDOW)(2011).
Jennifer Neville, Foster Provost.” Predictive modeling using social networks”:14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008).
Jiawei Han, Yizhou Sun, Xifeng Yan, Philip S. Yu.:”Mining Knowledge from Databases: An information Network Analysis Approach”: ACM SIGMOD Conference tutorial, Indianapolis, Indiana (2010).
Knowledge Discovery Laboratory Dataset. DBLP (http://www.informatik.uni-trier.de/- Iey/db/index.html/). Retrieved Sep. 13, 2010 from DBLP database (2010).
Aydoğdu, Ş. (2020). EDUCATIONAL DATA MINING STUDIES IN TURKEY: A SYSTEMATIC REVIEW. Turkish Online Journal of Distance Education, 21 (3), 170-185 . DOI: 10.17718/tojde.762046.
Jain J.,Sahu S. and Sahu N., “Sentiment Analysis based on NLP using Learning Techniques”, 2nd International Conference on Advance data driven computing, Scopus Indexed CRC Press Taylor & Francis,12-13 January 2024.
Jain J.,Sahu S., and Dixit A.,” Brain Tumor Detection model based on CNN and Threshold Segmentation” International Journal of Experimental Research & Review, Vol. 32: 358-364 30 Aug 2023.
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