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)


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.


Clustering Euclidean distance Multi-view similarity Spatio-temporal data 


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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

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