Efficient Mining of High Utility Sequential Patterns Over Data Streams

Morteza Zihayat, Cheng-Wei Wu, Aijun An, Vincent S Tseng
January 2014

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
Journal article
Publication
Journal Article

This work focuses on the efficient discovery of sequential patterns with high utility values in streaming data environments.

Sequential Pattern Mining Data Streams High Utility Patterns Pattern Mining
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.