Maintaining the Maximum Normalized Mean and Applications in Data Stream Mining
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Concept drift detection via competence models
Artificial Intelligence
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It is recognized that change detection is an important feature in many data stream applications. An appealing approach is to reformulate the problem of change detec- tion in data streams to the successive application of two sample tests, as proposed in [7]. Usually the underlying data-generation process is unknown. Consequently, non- parametric tests like the Kolmogorov-Smirnov (KS) test are desirable. Maintenance of the KS-test statistic can be per- formed efficiently in O(log(n)) per example, where n is the window size. However this can only be achieved by assum- ing a fixed window size. Because there exist no any time optimal window size, it is highly desirable to obtain a vari- able size window algorithm. In this paper we propose an efficient approximate algorithm for the maintenance of the KS-test statistic under the optimal window size.