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An Efficient Trajectory Representative Generation Moving Web-Based Data Prediction Using Different Clustering Algorithms

Author

Listed:
  • Vishnu Kumar Mishra

    (Shri Shankaracharya Institute of Engineering and Technology, India)

  • Megha Mishra

    (Shri Shankaracharya Institute of Engineering and Technology, India & Chhattisgarh Swami Vivekanand Technical University, India)

  • Bhupesh Kumar Dewangan

    (O.P. Jindal University, Raigarh, India)

  • Tanupriya Choudhury

    (University of Petroleum and Energy Studies, India)

Abstract

This paper highlighted moving and trajectory object cluster (MOTRACLUS) algorithm and analyzed the sub-trajectories and real-trajectories algorithm for moving web-based data and suggested a new approach of moving elements. This paper evaluates the hurricane data measure and mass less data measure entropy of trajectories objects of moving data of Chhattisgarh location. The paper covered prediction generation with their distance cluster minimum description length (MDL) algorithm and other corresponding distance cluster (CLSTR) algorithm. This paper highlighted the k-nearest algorithm with least cluster section (LCSS) model and dimensional Euclidean of MDL algorithm. The algorithm consists of two parts, that is, partitioning and grouping phase. This paper develops and enhances a cluster of trajectory objects and calculates the actual distance of moving objects. This algorithm works on the CLSTR algorithm and calculates trajectory movement of the object. In this, the authors evaluate the entropy of moving objects by consideration of the heuristic parameter.

Suggested Citation

  • Vishnu Kumar Mishra & Megha Mishra & Bhupesh Kumar Dewangan & Tanupriya Choudhury, 2022. "An Efficient Trajectory Representative Generation Moving Web-Based Data Prediction Using Different Clustering Algorithms," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 13(7), pages 1-16, October.
    • Handle: RePEc:igg:jismd0:v:13:y:2022:i:7:p:1-16
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