Mining top-k high utility patterns over data streams

Morteza Zihayat, Aijun An
January 2014

Abstract

This paper presents novel algorithms for mining top-k high utility patterns over data streams, addressing the challenges of real-time pattern discovery in streaming data environments.

Type
Journal article
Publication
Information Sciences

This work introduces efficient algorithms for discovering high utility patterns in streaming data environments.

Data 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.