Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data
  Message importance measure (MIM) is applicable to characterize the importance
of information in the scenario of big data, similar to entropy in information
theory. In fact, MIM with a variable parameter can make an effect on the
characterization of distribution. Furthermore, by choosing an appropriate
parameter of MIM, it is possible to emphasize the message importance of a
certain probability element in a distribution. Therefore, parametric MIM can
play a vital role in anomaly detection of big data by focusing on probability
of an anomalous event. In this paper, we propose a parameter selection method
of MIM focusing on a probability element and then present its major properties.
In addition, we discuss the parameter selection with prior probability, and
investigate the availability in a statistical processing model of big data for
anomaly detection problem.
