Mining high utility sequential patterns from evolving data streams

Morteza Zihayat, Cheng-Wei Wu, Aijun An, Vincent S Tseng
January 2015

Abstract

This paper presents methods for mining high utility sequential patterns from evolving data streams, addressing the dynamic nature of streaming data.

Type
Inbook
Publication
Proceedings of the ASE BigData & SocialInformatics 2015

This work addresses the challenges of mining sequential patterns from evolving data streams.

Sequential Pattern Mining Data Streams Evolving Data 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.