Efficiently mining high utility sequential patterns in static and streaming data

Morteza Zihayat, Cheng-Wei Wu, Aijun An, Vincent S Tseng, Chien Lin
January 2017

Abstract

This paper presents comprehensive algorithms for efficiently mining high utility sequential patterns in both static and streaming data environments.

Type
Journal article
Publication
Intelligent Data Analysis

This work provides unified approaches for high utility sequential pattern mining across different data environments.

Sequential Pattern Mining Data Streams Static Data High Utility Patterns Data Analysis
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.