Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

  • Mohammad Iqbal (SCOPUS ID: 24764478100), National Taiwan University of Science and Technology
  • Chandrawati Putri Wulandari National Taiwan University of Science and Technology
  • Wawan Yunanto Politeknik Caltex Riau
  • Ghaluh Indah Permata Sari National Taiwan University of Science and Technology
Keywords: Sequential Patterns; Rare Patterns; Activity Recognition; Multi-class


Discovering rare human activity patterns—from triggered motion sensors deliver peculiar information to notify people about hazard situations. This study aims to recognize rare human activities using mining non-zero-rare sequential patterns technique. In particular, this study mines the triggered motion sensor sequences to obtain non-zero-rare human activity patterns—the patterns which most occur in the motion sensor sequences and the occurrence numbers are less than the pre-defined occurrence threshold. This study proposes an algorithm to mine non-zero-rare pattern on human activity recognition called Mining Multi-class Non-Zero-Rare Sequential Patterns (MMRSP).  The experimental result showed that non-zero-rare human activity patterns succeed to capture the unusual activity. Furthermore, the MMRSP performed well according to the precision value of rare activities.


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How to Cite
Iqbal, M., Wulandari, C., Yunanto, W., & Sari, G. (2019). Mining Non-Zero-Rare Sequential Patterns On Activity Recognition. Jurnal Matematika "MANTIK", 5(1), 1-9.