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Novel Algorithms for Graph Clustering Applied to Human Activities

Author

Listed:
  • Nebojsa Budimirovic

    (Academy of Applied Studies Sabac, Dobropoljska 5, 15000 Sabac, Serbia)

  • Nebojsa Bacanin

    (Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia)

Abstract

In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters ( T ). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a starting point for forming the complete weighted graph. Using this graph, the proposed algorithms, as well as other well-known clustering algorithms, are tested. The best results are obtained using novel IBC2 algorithm: T = 96.43%, Rand Index ( RI ) 0.966, precision rate ( P ) 0.918, recall rate ( R ) 0.929 and balanced F-measure ( F ) 0.923.

Suggested Citation

  • Nebojsa Budimirovic & Nebojsa Bacanin, 2021. "Novel Algorithms for Graph Clustering Applied to Human Activities," Mathematics, MDPI, vol. 9(10), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1089-:d:552848
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