Efficiently mining high utility sequential patterns in static and streaming data
Abstract
This paper presents comprehensive algorithms for efficiently mining high utility sequential patterns in both static and streaming data environments.
Type
Publication
Intelligent Data Analysis
This work provides unified approaches for high utility sequential pattern mining across different 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.