Russel Pears: “Mining Data Streams: Issues, Challenges and Some Solutions”

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Speaker:          Russel Pears*

Date:     Tuesday, August 29

Time:    3:30-4:30 p.m.

Location:          Rice Hall, room 242

Host:                 Tom Horton (tbh3f)

Title:  Mining Data Streams: Issues, Challenges and Some Solutions

Abstract:   Data streams present unique challenges in terms of mining and knowledge extraction. Due to the open ended and fast data arrival rates associated with many data streams, standard machine learning approaches cannot be applied due to memory and speed constraints. In addition to these challenges, very often data streams are dynamic in nature with the underlying data distribution being subject to change over time.

In the first part of this talk I will touch on some of the methods that have been proposed to deal with the issues listed above. The second part of the talk will concentrate on my recent approach which explores a solution based on sensing stream volatility and tailoring the mining strategy to the level of volatility in the stream. Preliminary results from an experimental study have revealed that significant speed ups over state of the art approaches can be achieved while maintaining prediction accuracy.

About the speaker:  Russel Pears is currently attached to the Department of Computer Science at the Auckland University of Technology (AUT) in New Zealand. Russel’s career spans more than 3 decades in tertiary education, the last 16 of which has been at AUT University. During this period he has taught across a wide spectrum of courses in the Computer Science curriculum. He has also held senior leadership positions such as Programme Leader for the MSc and PhD programmes run by the School of Engineering. Computing and Mathematical Sciences at AUT University.

Russel’s research interests are in the Data Mining and Machine Learning areas where he has published widely in peer reviewed International conferences and journals. He currently supervises 4 Doctoral students and 2 MSc students in their thesis research.

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