Efficient Mining of High Utility Sequential Patterns Over Data Streams
Abstract
This paper presents efficient algorithms for mining high utility sequential patterns from data streams, addressing the computational challenges of real-time sequential pattern discovery.
Type
Publication
Journal Article
This work focuses on the efficient discovery of sequential patterns with high utility values in streaming data environments.
Morteza Zihayat
Principal Investigator
Dr. Morteza Zihayat is an Assistant Professor in the School of Information Technology Management at Toronto Metropolitan University and the Director of the Human-Centered Machine Intelligence Lab.