Online Submission!

Open Journal Systems

EMPIRICAL ANALYSIS OF DATA MINING TECHNIQUES FOR SOCIAL NETWORK WEBSITES

S.G.S Fernando, S.N. Perera

Abstract


Social networks allow users to collaborate with others. People of similar backgrounds and interests meet and cooperate using these social networks, enabling them to share information across the world. The social networks contain millions of unprocessed raw data. By analyzing this data new knowledge can be gained. Since this data is dynamic and unstructured traditional data mining techniques will not be appropriate. Web data mining is an interesting field with vast amount of applications. With the growth of online social networks have significantly increased data content available because profile holders become more active producers and distributors of such data. This paper identifies and analyzes existing web mining techniques used to mine social network data.

Full Text:

PDF

References


E.K Clemons., “ The Future of Advertising and the Value of Social Network Websites: Some Preliminary Examinations”. Minneapolis, Minneosta, USA, 2007.

D. Boyd., “Social Network Sites: Definition, History, and Scholarship”,Journal of Computer-Mediated Communication 13 (1), 2007.

“Social Network Marketing: The Basics” Available: http://www.labroots.com/Social_Networking_the_Basics.pdf [Aug 01,2013]. [4] J. Rennie, G. Zorpette, “The Social Era of the Web Starts Now,” IEEE Spectrum, June 2011. Available: http://spectrum.ieee.org/telecom/internet/the-social-era-of-the-web-starts-now [Aug 01, 2013]

J.W. Seifert. “Data Mining: An Overview”, CRS Report for Congress, 2004 [Online] Available: http://www.fas.org/irp/crs/RL31798.pdf [Sep 05 2013]

H. Kob, G, Tan, “Data Mining Applications in Healthcare” [Online] Available: http://www.himss.org/content/files/Code%20109_Data%20mining%20in%20healthcare_JHIM_.pdf [Nov 10 2010]

M Hart. “Progress of organisational data mining in South Africa”, Department of Information Systems, University of Cape Town, South

COMPUSOFT, An international journal of advanced computer technology, 3 (2), February-2014 (Volume-III, Issue-II)

Africa, 2006. [Online] Available: http://intranet.inria.fr/international/arima/006/pdf/arima00601.pdf [Jun 5, 2011]

E.Veerman, T.Lachev, D.Sarka, Microsoft SQL Server 2008- Bussiness Intelligence Development and Manitenance, Microsof, PHI Learning Private Limited, pp 372 – 380, 2009. [9] C. Yu, X. Ying, “Application of Data Mining Technology in E-Commerce” . in IEEE Int. Conf. on International Forum on Computer Science-Technology and Applications, pp.291-293, 2009.

G. Lappas G. “From Web Mining to Social Multimedia Mining.”, IEEE International Conference on Advances in Social Networks Analysis and Mining , pp 336 – 343, 2011. [11] J. Srivastava, Cooley R., Deshpande M., and Tan P.N., "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," SIGKKD Explorations, 2000. [12] S. Madria, S. Bhowmick , Research issues in web data mining, DataWarehousing and Knowledge Discovery Lecture Notes in Computer Science Volume 1676, pp 303-312 , 1999.

J. Sirivastava, Web Mining :Accomplishments & Future Directions, Available : http://www.ieee.org.ar/downloads/Srivastava-tut-paper.pdf [Nov 06 2013] [14] F. Bonchi, C. Castillo, A. Gionis, A. Jaimes ,“Social Network Analysis and Mining for Business Applications”, Yahoo! Research Barcelona. [15] M Eirinaki , “Web Mining : A Road trip”, 2008. Available : http://www.ualberta.ca/~golmoham/SW/web%20mining%2023Jan2008/WEB%20MINING%20A%20ROADMAP.pdf [Nov 01 2013] [16] M. Lahiri, T.Y. Berger-Wolf, “Mining Periodic Behavior in Dynamic Social Networks”, Eighth IEEE International Conference on Data Mining, pp 375-382, 2008. [17] R. Bourqui, F. Gilbert, P. Simonetto, F. Zaidi, U. Sharan, F. Jourdan, “ Detecting structural changes and command hierarchiesin dynamic social networks”, IEEE Advances in Social Network Analysis and Mining, pp 83-89, 2009. [18] K. Borgwardt, X. Yan: “Graph Mining and Graph Kernels”, KDD, 2008.

J. Kleinberg, et al.: “The web as a graph: Measurements, models andmethods”, COCOON, 1998 [20] J. Leskovec and C. Faloutsos, “Mining Large Graphs”, TutorialECML/PKDD, 2007

R.M. Assunção, M.C. Neves, G. Câmara, and C.C.Freitas. Efficient regionalization techniques for socioeconomicgeographical units using minimum spanningtrees, In International Journal of GeographicalInformation Science, v. 20, n. 7, pp 797–811,2006.

V.S.A. Menezes, R.T. Silva, M.F. Souza, J. Oliveira,C.E.R. Mello, J.M. Souza, G. Zimbrao. “Mining andAnalyzing Organizational Social Networks UsingMinimum Spanning Tree”. COOPIS, 2008.

V. Stroele, J. Oliveira, G. Zimbrão, J.M. Souza, “Mining and Analyzing Multirelational Social Networks”, IEEE International Conference on Computational Science and Engineering, pp 711-716, 2009.

Y. Zhang,Z. Wang,C. Xia “Identifying Key Users for Targeted Marketing by MiningOnline Social Network”, IEEE 24th International Conference on Advanced Information Networking and Applications Workshops, pp 644-649,2010.

C. K. Leung, C. L. Carmichael, “Exploring Social Networks: A Frequent Pattern Visualization Approach”, IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and TrustPp 419- 424, 2010.

D. Ediger, K..Jiang ,J. Riedy D. A. Bader, C. Corley, R. Farber, W. N. Reynolds, “ Massive Social Network Analysis:Mining Twitter for Social Good”,IEEE 39th International Conference on Parallel Processing, pp 583-593, 2010.

S. Abrol, L. Khan, “TweetHood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for LocationMining”, IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust, pp 153-160, 2010.

J. Chen, J. Fagnan, R. Goebel, R.Rabbany, F. Sangi, M.Takaffoli, E.Verbeek, O.Za¨ıane, “Meerkat: Community Mining with Dynamic Social Networks” , IEEE International Conference on Data Mining Workshops, pp 1377-1380, 2010.

N. Koochakzadeh, A. Sarraf,K. Kianmehr, J. Rokne, R. Alhajj, “NetDriller: A Powerful Social Network Analysis Tool”,11th IEEE International Conference on Data Mining Workshops, pp 1235-1238, 2011.

H. Asadi, C. M˚artenson, P. Svenson, M. Sk¨old,, “The HiTS/ISAC Social Network Analysis Tool”, IEEE European Intelligence and Security Informatics Conference, pp 291-296, 2012.

M. Bilgic , L. Getoor . B. Shneiderman, “D-Dupe: An Interactive Tool for Entity Resolution

COMPUSOFT, An international journal of advanced computer technology, 3 (2), February-2014 (Volume-III, Issue-II)

in Social Networks”, IEEE Symposium on Visual Analytics Science and Technology, pp 43-50 2006. [32] S. White, P. Smyth, “Algorithms for estimating relativeimportance in networks,” International Conference on KnowledgeDiscovery and Data Mining, vol. Proceedings of theninth ACM SIGKDD international conference on Knowledgediscovery and data mining, no. 2, pp. 266 – 275, 2003. [33] J. Kemeny and J. Snell, Finite Markov Chains. Springer, Verlag, 1976. [34] T. Crnovrsanin, C.D. Correa, K. L. Ma, “Social Network Discovery based on Sensitivity Analysis”, IEEE Advances in Social Network Analysis and Mining , pp 107-112, 2009. [35] F. Farazi, V. Maltese, F. Giunchiglia, and A. Ivanyukovich, “Afaceted ontology for a semantic geo-catalogue,” in Proceedings ofthe 8th extended semantic web conference on The semanicweb:research and applications - Volume Part II, ser. ESWC’11. Berlin,Heidelberg: Springer-Verlag,, pp. 169–182, 2011. [36] M. Hadzic and E. Chang, T. S. Dillon,E. Chang, R. Meersman, and K. Sycara, Eds. Berlin, Heidelberg:Springer-Verlag, Web Semantics for Intelligent and DynamicInformation Retrieval Illustrated within the Mental Health Domain,pp. 260–275, Available: http://dx.doi.org/10.1007/978-3-540-89784-2 10 [Sep 03 2013].

M. Opuszko, J. Ruhland, “Classification Analysis in Complex Online SocialNetworks Using Semantic Web Technologies”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp 1032-1039, 2012.

D. A. Ostrowski, “Semantic Social Network Analysis for TrendIdentification”, IEEE Sixth International Conference on Semantic Computing, pp 178-185, 2012.

Zhou, C. Chen, H. Tong , “Learning aProbalisitic Semantic Mdoel from Heterogeneous SocialNetworks for Relationship Identification Tools withArtificial Intelligence”,ICTAI 08. 20th IEEEInternational Conference Vol 1, 2008.

A.K.Tushar, , P.S.Thilagam, “An RDF Approach forDiscovering the Relevant Semantic Associations in a SocialNetwork Advanced Computing and Communications”,16th International, 2008.

Peng, Huan-Kai; Zhu, Jiang; Piao, Dongzhen; Yan,Rong; Zhang, Ying, “Retweet Modeling Using Conditional Random Fields “,Data Mining Workshops (ICDMW), 2011 IEEE

thInternational Conference,pp 336 – 343, 2011.

K. Sugiyama, H. ,.; Ohsaki, MImase, T. Yagi,J. Murayama, “NMF: Network Mining Framework UsingTopological Structure of Complex Network”s, pp 210 – 211,2008.

A. Bartal, E. Sasson, G. Ravid, “Predicting Link s in Social Networks using Text Mining and SNA” ,Advances in Social Network Analysis and Mining, pp 131-136 , 2009.

J. Surma , A. Furmanek, "Improving marketing response by data mining insocial network”, International Conference on Advances in Social Networks Analysis and Mining, pp 446-451, 2010.

W. Xue, J. Shi, Bo Yang , “X-RIME: Cloud-Based Large Scale Social Network Analysis”, IEEE International Conference on Services Computing , pp 506-513, 2010.

P. Nancy, R. G. Ramani, S. G. Jacob, " Mining of Association Patterns in Social Network Data (Face Book 100 Universities) through Data Mining Techniques and Methods ", Advances in Computing and Information TechnologyAdvances in Intelligent Systems and Computing Volume 178, pp 107-117 , 2013.

“Markov chains”, [Online]http://en.wikipedia.org/wiki/Markov_chain [Nov 6 2013]

P. Nancy, R. G. Ramani, “Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 universities) through Data Mining Techniques and Methods “,International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.5, PP 71-82, 2012

P. Nancy, Dr. R.G. Ramani: A Comparison on Performance of Data Mining Algorithms in Classification of Social Network Data”, International Journal of Computer Applications (IJCA),32(8): 47-54, October 2011. Published by Foundation of Computer Science, New York, USA.

A. Khan, Y. Wu, X. Yan, “Emerging Graph Queries In Linked Data” Avaialbe : http://www.cs.ucsb.edu/~arijitkhan/Papers/ICDE12_graph_queries.pdf [Nov 30]

S. Sharma, R.K. Gupta , “Improved BSP Clustering Algorithm for Social Network Analysis”,International Journal of Grid and

COMPUSOFT, An international journal of advanced computer technology, 3 (2), February-2014 (Volume-III, Issue-II)

Distributed Computing ,Vol. 3, No. 3, September, pp 67-76, 2010 .

Z. Markov, D. T. Larose, Data Mining the Web: Uncovering Patterns in Web Content, Structure and Usage, Wiley, 2007.

J. Weng ,E. Lim ,J. Jiang ,Q. He ,"TwitterRank: finding topic-sensitive influential twitterers", WSDM '10 Proceedings of the third ACM international conference on Web search and data mining , pp 261-270 ,2010.

T. H. Haveliwala ,"Topic-sensitive PageRank" , Proceeding WWW '02 Proceedings of the 11th international conference on World Wide Web, pp 517-526 ,2002.

F. Yang ,Z. Xu, S. Li, X. Li ,"Social network mining based on improved vector space model",Proceeding ICIMCS '10 Proceedings of the Second International conference on Internet Multimedia Computing and Service pp 118-121 , 2010

F. Huang, N. Xiao, X. Cheng, R. Xiao, "An Approach to Mining Social Networks in Chat Room",Journal of Computational Information Systems 7:1 (2011) 135-143, Available : http://www.Jofcis.com , 2011

P. Cortez, Data mining with neural networks and support vector machines using the R/rminer tool Published in:· ProceedingICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects pp 572-583,2010




DOI: http://dx.doi.org/10.6084/ijact.v3i2.261

Refbacks

  • There are currently no refbacks.




Copyright (c) 2015 COMPUSOFT "An International Journal of Advanced Computer Technology"