Discovering generalized association rules from Twitter

Intelligent Data Analysis

Volumn 17, Issue 4, 2013, Pages 627-648

  Cagliero, Luca ^a   Fiori, Alessandro ^b  

^a POLITECNICO DI TORINO   (Italy)

^b CANDIOLO CANCER INSTITUTE   (Italy)

Author keywords

generalized association rule mining; Social network analysis; taxonomy inference; user generated content

Indexed keywords

CONTEXTUAL FEATURE; GENERALIZED ASSOCIATION RULE MINING; GENERALIZED ASSOCIATION RULES; ON-LINE COMMUNITIES; SUPPORTING KNOWLEDGE; SYNTHETIC DATASETS; TAXONOMY GENERATION; USER-GENERATED CONTENT;

ASSOCIATION RULES; BEHAVIORAL RESEARCH; TAXONOMIES;

SOCIAL NETWORKING (ONLINE);

EID: 84881430532     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/IDA-130597     Document Type: Article

References (40)

1. R. Agrawal, T. Imielinski and A.N. Swami, Mining association rules between sets of items in large databases, in: SIGMOD Conference (1993), 207-216.
2. R. Agrawal and G. Psaila, Active data mining, in: Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining (1995), 3-8.
3. E. Baralis, L. Cagliero, T. Cerquitelli, V. D'Elia and P. Garza, Support driven opportunistic aggregation for generalized itemset extraction, in: 5th IEEE International Conference of Intelligent Systems (2010), 102-107.
4. E. Baralis, L. Cagliero, T. Cerquitelli, P. Garza and M. Marchetti, CAS-MINE: Providing personalized services in context-aware applications by means of generalized rules, Knowledge and Information Systems 28(2) (2011), 283-310. ISSN 0219-1377.
5. S. Baron, M. Spiliopoulou and O. GÃijnther, Efficient monitoring of patterns in data mining environments, Advances in Databases and Information Systems of Lecture Notes in Computer Science 2798 (2003), 253-265.
6. P. Basile, D. Gendarmi, F. Lanubile and G. Semeraro, Recommending smart tags in a social bookmarking system, Bridging the Gep between Semantic Web and Web 2 (2007), 22-29.
7. M. Bender, T. Crecelius, M. Kacimi, S. Michel, T. Neumann, J.X. Parreira, R. Schenkel and G. Weikum, Exploiting social relations for query expansion and result ranking, in: IEEE 24th International Conference on Data Engineering Workshop (2008), 501-506.
8. F. Benevenuto, T. Rodrigues, M. Cha and V. Almeida, Characterizing user behavior in online social networks, in: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference (2009), 49-62.
9. S. Bird, E. Klein and E. Loper, Natural language processing with python, O'Reilly Media, 2009.
10. A.S. Bozkir, S.G. Mazman and E.A. Sezer, Identification of user patterns in social networks by data mining techniques: Facebook case, Technological Convergence and Social Networks in Information Management (2010), 145-153.
11. M. Bttcher, D. Nauck, D. Ruta and M. Spott, Towards a framework for change detection in data sets, in: Research and Development in Intelligent Systems XXIII (2007), 115-128.
12. L. Cagliero, Discovering temporal change patterns in the presence of taxonomies, IEEE Transactions on Knowledge and Data Engineering 25(3) (2013), 541-555. ISSN 1041-4347.
13. W.A.V.B.D. Caragea and W.H. Hsu, Ontology-aware classification and association rule mining for interest and link prediction in social networks, 2009.
14. T. Cerquitelli, A. Fiori and A. Grand, Community-contributed media collections: Knowledge at our fingertips, Community-Built Databases: Research and Development (2011), 21.
15. M. Cheong and V. Lee, Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base, Proceeding of the 2nd ACM Workshop on Social Web Search and Mining (2009), 1-8.
16. C. Clifton, R. Cooley and J. Rennie, TopCat: Data mining for topic identification in a text corpus, IEEE Transactions on Knowledge and Data Engineering 16(8) (2004), 949-964.
17. B. Cui, J. Yao, G. Cong and Y. Huang, Evolutionary taxonomy construction from dynamic tag space, Web Information Systems Engineering (2010), 105-119.
18. S.C. Gates, W. Teiken and K.F. Cheng, Taxonomies by the numbers: Building high-performance taxonomies, in: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (2005), 568-577. (Pubitemid 44022231)
19. L. Guo, E. Tan, S. Chen, X. Zhang and Y.E. Zhao, Analyzing patterns of user content generation in online social networks, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), 369-378.
20. J. Han and Y. Fu, Mining multiple-level association rules in large databases, IEEE Transactions on Knowledge and Data Engineering 11(5) (2002), 798-805.
21. P. Heymann and H. Garcia-Molina, Collaborative creation of communal hierarchical taxonomies in social tagging systems, Stanford InfoLab Technical Report, (10), 2006.
22. P. Heymann, D. Ramage and H. Garcia-Molina, Social tag prediction, in: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2008), 531-538.
23. D. Ienco and R. Meo, Towards the automatic construction of conceptual taxonomies, Data Warehousing and Knowledge Discovery (2008), 327-336.
24. R. Kimball, M. Ross and R. Merz, The data warehouse toolkit: the complete guide to dimensional modeling, Wiley, 2002.
25. Q. Li, J.Wang, Y.P. Chen and Z. Lin,User comments for news recommendation in forum-based social media, Information Sciences (2010).
26. X. Li, L. Guo and Y.E. Zhao, Tag-based social interest discovery, in: Proceeding of the 17th International Conference on World Wide Web (2008), 675-684.
27. Y.-R. Lin, Y. Chi, S. Zhu, H. Sundaram and B.L. Tseng, Analyzing communities and their evolutions in dynamic social networks, ACM Transaction on Knowledge Discovery from Data 3 (2009), 8:1-8:31.
28. M. Mathioudakis and N. Koudas, TwitterMonitor: Trend detection over the twitter stream, in: Proceedings of the 2010 International Conference on Management of Data ACM, (2010), 1155-1158.
29. I. Pramudiono and M. Kitsuregawa, FP-tax: Tree structure based generalized association rule mining, in: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2004), 63.
30. C. Schmitz, A. Hotho, R. Jaschke and G. Stumme, Mining association rules in folksonomies, Data Science and Classification (2006), 261-270.
31. E. Schwarzkopf, D. Heckmann, D. Dengler and A. Kroner, Mining the structure of tag spaces for user modeling, in: Proceedings of Workshop on Data Mining for User Modeling (2007), 63-75.
32. A. Shepitsen, J. Gemmell, B. Mobasher and R. Burke, Personalized recommendation in social tagging systems using hierarchical clustering, in: Proceedings of the ACM Conference on Recommender Systems (2008), 259-266.
33. R. Srikant and R. Agrawal, Mining generalized association rules, in: VLDB (1995), 407-419.
34. P.N. Tan, V. Kumar and J. Srivastava, Selecting the right interestingness measure for association patterns, in: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002), 41.
35. P.N. Tan, M. Steinbach, V. Kumar et al., Introduction to data mining, Pearson Addison Wesley Boston, 2006.
36. Y. Tao andM.T. Özsu,Mining frequent itemsets in time-varying data streams, in: Proceeding of the 18th ACMConference on Information and Knowledge Management CIKM, (2009), 1521-1524.
37. TPC-H, The TPC benchmark H, Transaction Processing Performance Council, 2009.
38. Y. Xue, C. Zhang, C. Zhou, X. Lin and Q. Li, An effective news recommendation in social media based on users' preference, International Workshop on Education Technology and Training 1 (2009), 627-631.
39. Z. Yin, R. Li, Q. Mei and J. Han, Exploring social tagging graph for web object classification, in: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), 957-966.
40. M.J. Zaki, S. Parthasarathy, M. Ogihara,W. Li et al., New algorithms for fast discovery of association rules, Proceddings of the 3rd International Conference on Knowledge Discovery and Data Mining 20 (1997).