  Human behavior modeling deals with learning and understanding behavior
patterns inherent in humans' daily routines. Existing pattern mining techniques
either assume human dynamics is strictly periodic, or require the number of
modes as input, or do not consider uncertainty in the sensor data. To handle
these issues, in this paper, we propose a novel clustering approach for
modeling human behavior (named, MTpattern) from time-series data. For mining
frequent human behavior patterns effectively, we utilize a three-stage
pipeline: (1) represent time series data into a sequence of regularly sampled
equal-sized unit time intervals for better analysis, (2) a new distance measure
scheme is proposed to cluster similar sequences which can handle temporal
variation and uncertainty in the data, and (3) exploit an exemplar-based
clustering mechanism and fine-tune its parameters to output minimum number of
clusters with given permissible distance constraints and without knowing the
number of modes present in the data. Then, the average of all sequences in a
cluster is considered as a human behavior pattern. Empirical studies on two
real-world datasets and a simulated dataset demonstrate the effectiveness of
MTpattern with respect to internal and external measures of clustering.
