Distributed and parallel high utility sequential pattern mining

Morteza Zihayat, Zane Zhenhua Hut, Aijun An, Yonggang Hut
January 2016

Abstract

This paper presents distributed and parallel algorithms for high utility sequential pattern mining, addressing scalability challenges in big data environments.

Type
Conference paper
Publication
2016 IEEE International Conference on Big Data (Big Data)

This work introduces distributed and parallel approaches for scalable sequential pattern mining in big data environments.

Sequential Pattern Mining Distributed Computing Parallel Computing Big 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.