Title: Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data,
Abstract: 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.