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This paper focuses on concept drift in business intelligence and data-driven decision support systems (DSSs). The assumption of a fixed distribution in the data renders conventional static DSSs inaccurate and unable to make correct decisions when concept drift occurs. However, it is important to know when, how, and where concept drift occurs so a DSS can adjust its decision processing knowledge to...
Current methods often predefine fixed roles of members and only detect fixed hierarchy structures that are not consistent with real-world communities; methods with hand-crafted thresholds bring difficulties in real applications, while choosing the community corresponding to the maximal belonging coefficient for each node results in a single boundary and neglects the multi-resolution of communities...
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