A self-adaptive clustering scheme with a time-decay function for microblogging text mining

Communications in Computer and Information Science

Volumn 185 CCIS, Issue PART 2, 2011, Pages 62-71

  Lee, Chung Hong ^a   Wu, Chih Hung ^a  

^a NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

Author keywords

microblogs; real time mining; self adaptive term weighting; social networks

Indexed keywords

CLUSTERING MODEL; CLUSTERING SCHEME; DAILY LIVES; INFORMATION ACQUISITIONS; MICROBLOGGING; MICROBLOGS; NATURAL PROPAGATION; ONLINE EVENT DETECTION; REAL-TIME MINING; SELF-ADAPTIVE; SEMANTIC FEATURES; SHORT MESSAGE; SIMILARITY MATRIX; SOCIAL NETWORKS; TEXT MINING; TEXT STREAMS;

INFORMATION TECHNOLOGY; SEMANTICS; SOCIAL NETWORKING (ONLINE);

DATA MINING;

EID: 79960138366     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-22309-9_8     Document Type: Conference Paper

References (18)

1. Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851-860. ACM, Raleigh (2010)
2. Heverin, T., Zach, L.: Microblogging for Crisis Communication: Examination of Twitter Use in Response to a, Violent Crisis in Seattle-Tacoma, Washington Area. In: Conference Microblogging for Crisis Communication: Examination of Twitter Use in Response to a 2009 Violent Crisis in Seattle-Tacoma, Washington Area (2009)
3. Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56-65. ACM, San Jose (2007)
4. Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., Rai, A., Madan, S.: User interests in social media sites: an exploration with micro-blogs. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 1823-1826. ACM, Hong Kong (2009)
5. Cheong, M., Lee, V.: Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base. In: Proceeding of the 2nd ACM Workshop on Social Web Search and Mining, pp. 1-8. ACM, Hong Kong (2009)
6. Honeycutt, C., Herring, S.C.: Beyond Microblogging: Conversation and Collaboration via Twitter. In: Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 1-10. IEEE Computer Society, Los Alamitos (2009)
7. Bakshy, E., Karrer, B., Adamic, L.A.: Social influence and the diffusion of user-created content. In: Proceedings of the Tenth ACM Conference on Electronic Commerce, pp. 325- 334. ACM, Stanford (2009)
8. Weng, J., Lim, E.-P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261-270. ACM, New York (2010)
9. Gong, L., Zeng, J., Zhang, S.: Text stream clustering algorithm based on adaptive feature selection. Expert Systems with Applications 38, 1393-1399 (2011)
10. Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 446-453. ACM, Washington, D.C (2004)
11. Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 28-36. ACM, Melbourne (1998)
12. Yang, Y., Carbonell, J.G., Brown, R.D., Pierce, T., Archibald, B.T., Liu, X.: Learning approaches for detecting and tracking news events. IEEE Intelligent Systems and their Applications 14, 32-43 (1999)
13. Brants, T., Chen, F., Farahat, A.: A System for new event detection. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 330-337. ACM, Toronto (2003)
14. Swan, R., Allan, J.: Automatic generation of overview timelines. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49-56. ACM, Athens (2000)
15. Joel, W.R., Yu, J., Thomas, E.P., Brian, A.K., Mark, T.E., Ali, R.H.: TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams. In: Conference TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams, pp. 258-263
16. Li, W.: Random texts exhibit Zipf's-law-like word frequency distribution. IEEE Transactions on Information Theory 38, 1842-1845 (1992)
17. Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Cascading Behavior in Large Blog Graphs. In: SIAM International Conference on Data Mining, SDM (2007)
18. Barabasi, A.-L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207-211 (2005)