Memory-adaptive high utility sequential pattern mining over data streams
Abstract
This paper presents a memory-adaptive approach for mining high utility sequential patterns over data streams, addressing memory constraints in streaming environments.
Type
Publication
Machine Learning
This work introduces memory-adaptive techniques for efficient sequential pattern mining in resource-constrained streaming environments.
Morteza Zihayat
Principal Investigator
Dr. Morteza Zihayat is a Canada Research Chair (CRC) in Human-Centered AI and Associate Professor at Toronto Metropolitan University, Faculty of Engineering and Architectural Science. He also holds appointments as Adjunct Associate Professor at the University of Waterloo (Management Sciences) and IBM Faculty Fellow at IBM Centre for Advanced Studies. He is the Director of the Human-Centered Machine Intelligence Lab.