Abstract:
This paper presents an algorithm for density estimation over non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian seque...Show MoreMetadata
Abstract:
This paper presents an algorithm for density estimation over non-stationary high-dimensional data streams. It is based on a blockized implementation of the Bayesian sequential partitioning (BSP) algorithm. We discuss how to decide the optimum block size, based on features of the stream, the application requirements, and the available resources. Simulation results are presented to show the applicability of the proposed method to non-stationary streams. It is also shown that the proposed framework satisfies the general design criteria for systems with the mission of online machine learning and data mining over data streams.
Date of Conference: 26-29 May 2019
Date Added to IEEE Xplore: 01 May 2019
Print ISBN:978-1-7281-0397-6
Print ISSN: 2158-1525