Unsupervised Activity Discovery and Characterization From Event-Streams
  We present a framework to discover and characterize different classes of
everyday activities from event-streams. We begin by representing activities as
bags of event n-grams. This allows us to analyze the global structural
information of activities, using their local event statistics. We demonstrate
how maximal cliques in an undirected edge-weighted graph of activities, can be
used for activity-class discovery in an unsupervised manner. We show how
modeling an activity as a variable length Markov process, can be used to
discover recurrent event-motifs to characterize the discovered
activity-classes. We present results over extensive data-sets, collected from
multiple active environments, to show the competence and generalizability of
our proposed framework.
