A Novel Multi-view Similarity for Clustering Spatio-Temporal Data

  • Vijaya Bhaskar Velpula
  • M. H. M. Krishna Prasad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

Abstract

With the enhanced usage of sensors and GPS devices, obtaining spatial and spatio-temporal data has become easy and analyses of these data in real-time applications are increasing day to day. Clustering is a data mining technique used for analyzing and obtaining unknown/hidden knowledge from the data/objects. Distance-based methods are helpful for analyzing and grouping the objects. In general, based on the type of data, Euclidean or Cosine distance-based techniques are used for grouping the data. Traditional techniques are point-based techniques and are based on single-view point, which may not produce efficient information and cannot be utilized for analyzing spatio-temporal objects. Hence, this paper presents a novel multi-view similarity technique for clustering spatio-temporal objects. Authors demonstrated the effectiveness of the proposed technique by adopting DBSCAN and implementing JDK1.2 on benchmarked datasets with respect to FMI indicator.

Keywords

Clustering Euclidean distance Multi-view similarity Spatio-temporal data 

References

  1. 1.
    Lei, C., Tamer, M.: Robust and fast similarity search for moving object trajectories. In: ACM SIGMOD Conference, pp. 491–502. ACM, New York (2005)Google Scholar
  2. 2.
    Nesrine, A., Lorna, S.: Graph Clustering: Complexity, Sequential and Parallel Algorithms. University of Alberta, Edmonton (1995)Google Scholar
  3. 3.
    Abhishek, K., Piyush, R., Hal, D.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems, pp. 1413–1421. Spain (2011)Google Scholar
  4. 4.
    Jae-Gil, L., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: ACM SIGMOD, pp. 593–604 (2007)Google Scholar
  5. 5.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Fourth International Conference on Foundations of Data Organization and Algorithms, pp. 69–84. London (1993)Google Scholar
  6. 6.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence matching in time-series databases. In: ACM SIGMOD International Conference on Management of Data, pp. 419–429. Minnesota (1994)Google Scholar
  7. 7.
    Hazarath, M., Lucio, I., Luca, C.: CAST: a novel trajectory clustering and visualization tool for spatio-temporal data. In: First International Conference on Intelligent Human Computer Interaction, pp.169–175. India (2009)Google Scholar
  8. 8.
    Lee, J., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: ACM SIGMOD International Conference on Management of Data, pp. 593—604. Beijing (2007)Google Scholar
  9. 9.
    Wei, X., Xin, L., Yihong, G.: Document clustering based on non-negative matrix factorization. Paper presented at the meeting of the SIGIR (2003)Google Scholar
  10. 10.
    Arindam, B., Inderjit, S.D., Joydeep, G., Sra, S.: Clustering on the unit hypersphere using von mises-fisher distributions. J. Mach. Learn. Res. 6, 1345–1382 (2005)MATHMathSciNetGoogle Scholar
  11. 11.
    Hongyuan, Z., Xiaofeng, H., Chris. H.Q.D., Ming, G.: Spectral relaxation for K-means clustering. In: Advances in Neural Information Processing Systems 14, pp. 1057–1064 (2001)Google Scholar
  12. 12.
    Jianbo, S., Jitendra, M.: Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  13. 13.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning KDD, pp. 269–274. ACM (2001)Google Scholar
  14. 14.
    Zhao, Y., Karypis, G.: Empirical and theoretical comparisons of selected criterion functions for document clustering. Mach. Learn. 55(3), 311–331 (2004)MATHCrossRefGoogle Scholar
  15. 15.
    Karypis, G.: CLUTO a Clustering Toolkit. Technical report, Department of Computer Science, University of Minnesota. http://glaros.dtc.umn.edu/~gkhome/views/cluto (2003)
  16. 16.
    Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: 17th National Conference Artificial Intelligence: Workshop of Artificial Intelligence for Web Search (AAAI), pp. 58–64. July (2000)Google Scholar
  17. 17.
    Prasad, V.V.D., Krishna Prasad, M.H.M., Velpula, V.B.: Evaluation of multi-view point similarity based document clustering. In: Third International Conference on Recent Trends in Engineering and Technology, pp. 132–135. India (2014)Google Scholar
  18. 18.
    Ahmad, A., Dey, L.: A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recogn. Lett. 28(1), 110–118 (2007)CrossRefGoogle Scholar
  19. 19.
    Ienco, D., Pensa, R.G., Meo, R.: Context-based distance learning for categorical data clustering. In: Eighth International Symposium. Intelligent Data Analysis (IDA). pp. 83–94 (2009)Google Scholar
  20. 20.
    T-Drive trajectory’s dataset downloaded on 8 Sep 2013. http://research.microsoft.com/apps/pubs/?id=152883
  21. 21.
    Fowlkes, E.B., Mallows, C.L.: A method for comparing two hierarchical clusterings. J. Am. Stat. Assoc. 78, 383 (1983)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Vijaya Bhaskar Velpula
    • 1
  • M. H. M. Krishna Prasad
    • 2
  1. 1.Department of Computer Science and EngineeringGECGunturIndia
  2. 2.Department of Computer Science and EngineeringUCEK, JNTUKKakinadaIndia

Personalised recommendations