Theory and techniques of data mining in CGF behavior modeling

Abstract

For the large quantity of data, rules and models generated in the course of computer generated forces (CGFs) behavior modeling, the common analytical methods are statistical methods based on the tactical rules, tactical doctrine and empirical knowledge. However, from the viewpoint of data mining, we can find many of these analytical methods are also each-and-every different data mining methods. In this paper, we survey the data mining theory and techniques that have appeared in the course of CGF behavior modeling from the viewpoint of data mining. Further, we redefine and reclassify these theories and techniques according to the research requirements of data mining (the knowledge structure of data mining). We aim to help the CGF investigators to learn the key points and the research methods of the data intelligent processing in the CGF behavior modeling, and thus we can provide the references for the colleagues who are engaged in the research and practice of the CGF behavior modeling by this article. We also introduce our ongoing work on the CGF behavior modeling based on data mining.

Oil Flow Analysis in the Maritime Silk Road Region Using AIS Data

“…The data that need to be cleaned are collectively called "dirty data." Dirty data consist of three types of data: incomplete data, erroneous data, and duplicate data [43][44][45]. The standards for data cleaning are presented in Table 2.…”