Fast adaptive kernel density estimator for data streams

The probability density function (PDF) is an effective data model for a variety of stream mining tasks. As such, accurate estimates of the PDF are essential to reducing the uncertainties and errors associated with mining results. The nonparametric adaptive kernel density estimator (AKDE) provides accurate, robust, and asymptotically consistent estimates of a PDF. However, due to AKDE’s extensive computational requirements, it cannot be directly applied to the data stream environment. This paper describes the development of an AKDE approximation approach that heeds the constraints of the data stream environment and supports efficient processing of multiple queries. To this end, this work proposes (1) the concept of local regions to provide a partition-based variable bandwidth to capture local density structures and enhance estimation quality; (2) a suite of linear-pass methods to construct the local regions and kernel objects online; (3) an efficient multiple queries evaluation algorithm; (4) a set of approximate techniques to increase the throughput of multiple density queries processing; and (5) a fixed-size memory time-based sliding window that updates the kernel objects in linear time. Comprehensive experiments were conducted with real-world and synthetic data sets to validate the effectiveness and efficiency of the approach.