Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the\ntraditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept\nevolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this\npaper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis\ndistance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and\nnovel class detection.
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