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Large amount of time series data generated by sensors and Web users is great source of contextual information. Detecting outliers with unusually high values in time series data is crucial for inferring about any events in the real world. In this work, we describe an infinite Poisson mixture model to detect events by identifying outliers in time series of count data. This unsupervised technique estimates...
Recognizing links between offender patterns is one of the most crucial skills of an investigator. Early recognition of similar patterns can lead to focusing resources, improving clearance rates, and ultimately saving lives in terms of digital forensics. In this paper we propose a forensics methodology using Markov chain during a given time interval for tracking and predicting the degree of criminal...
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