TweeProfiles: Detection of spatio-temporal patterns on twitter

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8933, Issue , 2014, Pages 123-136

  Cunha, Tiago ^a   Soares, Carlos ^a   Rodrigues, Eduarda Mendes ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

Clustering; Data Mining; Spatio temporal patterns; Visualization

Indexed keywords

CLUSTERING ALGORITHMS; DATA VISUALIZATION; FLOW VISUALIZATION; SOCIAL NETWORKING (ONLINE); VISUALIZATION;

APPROPRIATE DISTANCES; CLUSTERING; COMBINATION METHOD; DATA MINING PROCESS; INFORMATION SOURCES; ON-LINE SOCIAL NETWORKS; SPATIOTEMPORAL PATTERNS; VISUALIZATION PLATFORMS;

DATA MINING;

EID: 84918780959     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-14717-8_10     Document Type: Article

References (19)

1. Lee, C.-H., Yang, H.-C., Chien, T.-F., Wen, W.-S.: A Novel Approach for Event Detection by Mining Spatio-temporal Information on Microblogs. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 254–259 (2011)
2. Bosnjak, M., Oliveira, E.: TwitterEcho: a distributed focused crawler to support open research with twitter data. In: Proc. of the Intl. Workshop on Social Media Applications in News and Entertainment (SMANE 2012), ACM 2012 International World Wide Web Conference (2012)
3. Golder, S.: Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. HICSS 2010 Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, 1–10 (2010)
4. Abel, F., Gao, Q., Houben, G.J., Tao, K.: Analyzing Temporal Dynamics in Twitter Profiles for Personalized Recommendations in the Social Web. In: Proceedings of ACM WebSci 2011, 3rd International Conference on Web Science (2011)
5. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Elsevier Science & Technology, Massachussets (2006)
6. Ramakrishnan, R., T. Zhang, M.L.: BIRCH: an efficient data clustering method for very large databases. In: Procedings SIGMOD 1996 Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, vol. 1, pp. 103–114 (1996)
7. Karypis, G., Han, E.H., Kumar, V.: Chameleon: Hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999)
8. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
9. Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of 4th International Conference in Knowledge Discovery and Data Mining (KDD 1998), pp. 58–65 (1998)
10. Wang, W., Yang, J., Muntz, R.: STING: A statistical information grid approach to spatial data mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, pp. 186–195 (1997)
11. Akcora, C.G., Carminati, B., Ferrari, E.: Network and profile based measures for user similarities on social networks. In: 2011 IEEE International Conference on Information Reuse & Integration, pp. 292–298 (2011)
12. Dekker, A.: Conceptual Distance in Social Network Analysis. Journal of Social Structure 6 (2005)
13. Ryu, H., Lease, M., Woodward, N.: Finding and exploring memes in social media. In: Proceedings of the 23rd ACM conference on Hypertext and social media - HT 2012, p. 295 (2012)
14. Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
15. Lopes, A.A., Pinho, R., Paulovich, F.V., Minghim, R.: Visual text mining using association rules. Computers & Graphics 31(3), 316–326 (2007)
16. Rangrej, A., Kulkarni, S., Tendulkar, A.V.: Comparative study of clustering techniques for short text documents. In: Proceedings of the 20th International Conference Companion on World Wide Web - WWW 2011 (2011)
17. Lee, C.-H.: Mining spatio-temporal information on microblogging streams using a density-based online clustering method. Expert Systems with Applications 39(10), 9623–9641 (2012)
18. Mahdiraji, A.: Clustering data stream: A survey of algorithms. International Journal of Knowledge-Based and Intelligent Engineering Systems 13(2), 39–44 (2009)
19. Provost, F., Fawcett, T.: Data Science and its Relationship to Big Data and Data- Driven Decision Making. Big Data 1(1), 51–59 (2013)