Memory-bounded high utility sequential pattern mining over data streams

Morteza Zihayat, Y Chan, Aijun An
January 2015

Abstract

This technical report presents memory-bounded algorithms for high utility sequential pattern mining over data streams, addressing memory constraints in streaming environments.

Type
Report
Publication
Technical Report EECS-2015-04, York University

This technical report explores memory-efficient approaches for sequential pattern mining in streaming data.

Sequential Pattern Mining Data Streams Memory Management High Utility Patterns
Morteza Zihayat
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.