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Investigation of cluster validity indices for unsupervised human activity discovery
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Abstract:
An approach for unsupervised human activity discovery has been proposed in this paper. The approach automatically discover unknown activities from unlabeled data and has the ability to reject random activities. This ability will enable intelligent systems to discover and learn new activities autonomously. K-means is used to cluster a pool of unlabeled activity observations into groups of different activities. The system requires no prior knowledge of how many activities to be discovered. It uses cluster validity indices to automatically estimate the required number of clusters and further evaluate cluster homogeneity to accept clusters with homogenous activity and reject clusters with random activities. Experimental results showed the potential of the approach and identified suitable validity indices to achieve unsupervised human activity discovery.
Description:
Date:
2013
Authors:
Ong, Wee-Hong
Koseki, Takafumi
Palafox, Leon
Publisher:
CSREA Press
