Novel Algorithms for Graph Clustering Applied to Human Activities

Nebojsa Budimirovic and Nebojsa Bacanin
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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

Mathematics, 2021, vol. 9, issue 10, 1-17

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.

Keywords: graph clustering; the accuracy of formed clusters; inertial measurement units data; human activities clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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