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 an Assistant Professor in the School of Information Technology Management at Toronto Metropolitan University and the Director of the Human-Centered Machine Intelligence Lab.