Mining Non-Zero-Rare Sequential Patterns On Activity Recognition

Mantik: Jurnal Matematika

Mohammad Iqbal¹  Ghaluh Indah Permata Sari²  Chandrawati Putri Wulandari²  Wawan Yunanto³ 

[1] (SCOPUS ID: 24764478100), National Taiwan University of Science and Technology;National Taiwan University of Science and Technology;Politeknik Caltex Riau;

关键词: Sequential Patterns;    Rare Patterns;    Activity Recognition;    Multi-class;   

DOI  :  10.15642/mantik.2019.5.1.1-9

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown