Mining Non-Zero-Rare Sequential Patterns On Activity Recognition
DOI:
https://doi.org/10.15642/mantik.2019.5.1.1-9Keywords:
Sequential Patterns; Rare Patterns; Activity Recognition; Multi-classAbstract
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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