Penambangan pola non-zero-rare sekuensial pada pengenalan aktifitas

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)

Jurnal Matematika: MANTIK
Vol. 5 No. 1 (2019): Mathematics and Applied Mathematics

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.