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Message importance measure (MIM) is applicable to characterize the importanceof information in the scenario of big data, similar to entropy in informationtheory. In fact, MIM with a variable parameter can make an effect on thecharacterization of distribution. Furthermore, by choosing an appropriateparameter of MIM, it is possible to emphasize the message importance of acertain probability element in a distribution. Therefore, parametric MIM canplay a vital role in anomaly detection of big data by focusing on probabilityof an anomalous event. In this paper, we propose a parameter selection methodof MIM focusing on a probability element and then present its major properties.In addition, we discuss the parameter selection with prior probability, andinvestigate the availability in a statistical processing model of big data foranomaly detection problem.

Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data