Dayu Xu1,#, Jiaming Lü1,#, Xuyao Zhang2, Hongtao Zhang1,*
CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2557-2573, 2024, DOI:10.32604/cmc.2024.045932
Abstract Data stream clustering is integral to contemporary big data applications. However, addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research. This paper aims to elevate the efficiency and precision of data stream clustering, leveraging the TEDA (Typicality and Eccentricity Data Analysis) algorithm as a foundation, we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm. The original TEDA algorithm, grounded in the concept of “Typicality and Eccentricity Data Analytics”, represents an evolving and recursive method that requires no prior knowledge. While the… More >
Login
Register
