Advertisement

Learning Similarity Functions for Urban Events Detection by Mining Hotline Phone Records

  • Pengjie Ren
  • Peng Liu
  • Zhumin Chen
  • Jun Ma
  • Xiaomeng Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)

Abstract

Many cities around the world have established a platform, entitled public service hotline, to allow citizens to tell about city issues, e.g. noise nuisance, or personal encountered problems, e.g. traffic accident, by making a phone call. As a result of “crowd sensing”, these records contain rich human intelligence that can help to detect urban events. In this paper, we present an event detection approach to detect urban events based on phone records. Specifically, given a set of phone records in a period of time, we first learn a similarity matrix. Each element of the matrix is estimated as the probability that the corresponding pair of records describe the same event. Then, we propose an Improved Affinity Propagation (IAP) clustering approach which takes the similarity matrix as input and generates clusters as output. Each cluster is an urban event composed of several records. Extensive experiments demonstrate the great improvement of IAP on three standard datasets for clustering and the effectiveness of our event detection approach on real data from a hotline.

Keywords

Event Detection Data Mining Urban Computing Machine Learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rana, R., Chou, C., Kanhere, S., Bulusu, N., Hu, W.: Ear-phone: an end-to-end participatory urban noise mapping system. In: IPSN 2010 (2010)Google Scholar
  2. 2.
    Zheng, Y., Liu, T., Wang, Y., Zhu, Y., Chang, E.: Diagnosing new york citys noises with ubiquitous data. In: Ubicomp 2014 (2014)Google Scholar
  3. 3.
    Xie, Z., Yan, J.: Kernel density estimation of traffic accidents in a network space. Computers, Environment and Urban Systems (2008)Google Scholar
  4. 4.
    Li, J., Zhou, Y., Shang, W., Cao, C., Shen, Z., Yang, F., Xiao, X., Guo, D.: A cloud computation architecture for unconventional emergency management. In: Gao, Y., Shim, K., Ding, Z., Jin, P., Ren, Z., Xiao, Y., Liu, A., Qiao, S. (eds.) WAIM 2013 Workshops 2013. LNCS, vol. 7901, pp. 187–198. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Spina, D., Gonzalo, J., Amigó, E.: Learning similarity functions for topic detection in online reputation monitoring. In: SIGIR 2014 (2014)Google Scholar
  6. 6.
    Artiles, J., Amigó, E., Gonzalo, J.: The role of named entities in web people search. In: EMNLP 2009 (2009)Google Scholar
  7. 7.
    Xia, T.: Study on chinese words semantic similarity computation. Computer Engineering (2007)Google Scholar
  8. 8.
    Frey, B., Dueck, D.: Clustering by passing messages between data points. Science (2007)Google Scholar
  9. 9.
    Achtert, E., Goldhofer, S., Kriegel, H.-P., Schubert, E., Zimek, A.: Evaluation of clusterings–metrics and visual support. In: ICDE 2012 (2012)Google Scholar
  10. 10.
    Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval (2009)Google Scholar
  11. 11.
    Meilă, M.: Comparing clusterings by the variation of information. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 173–187. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning (2004)Google Scholar
  13. 13.
    Rezankova, H., Loster, T., Husek, D.: Evaluation of categorical data clustering. In: Mugellini, E., Szczepaniak, P.S., Pettenati, M.C., Sokhn, M. (eds.) AWIC 2011. AISC, vol. 86, pp. 173–182. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Rangrej, A., Kulkarni, S., Tendulkar, A.: Comparative study of clustering techniques for short text documents. In: WWW 2011 (2011)Google Scholar
  15. 15.
    Weng, J., Lee, B.: Event detection in twitterGoogle Scholar
  16. 16.
    Diao, Q., Jiang, J., Zhu, F., Lim, E.: Finding bursty topics from microblogs. In: ACL 2012 (2012)Google Scholar
  17. 17.
    Gao, Z., Song, Y., Liu, S., Wang, H., Wei, H., Chen, Y., Cui, W.: Tracking and connecting topics via incremental hierarchical dirichlet processes. In: ICDM 2011Google Scholar
  18. 18.
    Aggarwal, C., Zhai, C.: A survey of text clustering algorithms. In: Mining Text Data 2012 (2012)Google Scholar
  19. 19.
    Gupta, M., Li, R., Chang, K.: Towards a social media analytics platform: event detection and user profiling for twitter. In: WWW 2014 (2014)Google Scholar
  20. 20.
    AlSumait, L., Barbará, D., Domeniconi, C.: On-line lda: Adaptive topic models for mining text streams with applications to topic detection and tracking. In: ICDM 2008 (2008)Google Scholar
  21. 21.
    Wang, X., Zhu, F., Jiang, J., Li, S.: Real time event detection in twitter. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 502–513. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: WWW 2010 (2010)Google Scholar
  23. 23.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: Real-world event identification on twitterGoogle Scholar
  24. 24.
    Chen, F., Neill, D.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: SIGKDD 2014 (2014)Google Scholar
  25. 25.
    Rozenshtein, P., Anagnostopoulos, A., Gionis, A., Tatti, N.: Event detection in activity networks. In: SIGKDD 2014 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Pengjie Ren
    • 1
  • Peng Liu
    • 1
  • Zhumin Chen
    • 1
  • Jun Ma
    • 1
  • Xiaomeng Song
    • 1
  1. 1.Department of Computer Science and TechnologyShandong UniversityJinanChina

Personalised recommendations