Mining top-k high utility patterns over data streams

Morteza Zihayat, Aijun An
January 2013

Abstract

This technical report presents algorithms for mining top-k high utility patterns over data streams, addressing real-time pattern discovery challenges.

Type
Misc
Publication
Department of Computer Science and Engineering, York University

This technical report covers algorithms for top-k high utility pattern mining in streaming data.

Data Mining Data Streams High Utility Patterns Top-K Patterns Technical Report
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.