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StreamKDD'10 - First International Workshop on Novel Data Stream Pattern Mining Techniques
Sunday, July 25, 2010 (afternoon session)

Online workshop proceedings at the ACM Digital Library.

Workshop Description
Data stream mining gained in importance over the last years because it is indispensable for many real applications such as prediction and evolution of weather phenomena; security and anomaly detection in networks; evaluating satellite data; and mining health monitoring streams. Stream mining algorithms must take account of the unique properties of stream data: infinite data, temporal ordering, concept drifts and shifts, demand for scalability etc.

This workshop brings together scholars working in different areas of learning on streams, including sensor data and other forms of accumulating data. Most of the papers in the next pages are on unsupervised learning with clustering methods. Issues addressed include the detection of outliers and anomalies, evolutionary clustering and incremental clustering, learning in subspaces of the complete feature space and learning with exploitation of context, deriving models from text streams and visualizing them.

Workshop Program

Prince William (3rd floor)

2:00 pm - 2:15 pm
Margaret H. Dunham, Michael Hahsler, Myra Spiliopoulou
2:15 pm - 2:40 pm
Keynote speech - Research Issues in Mining Multiple Data Streams
Le Gruenwald, University of Oklahoma and National Science Foundation
2:40 pm - 3:00 pm
Fully-Decentralized Computation of Aggregates over Data Streams (Best Paper Award)
Luca Becchetti, Ilaria Bordino, Stefano Leonardi and Adi Rosen
3:00 pm - 3:20 pm
Detecting Outliers on Arbitrary Data Streams using Anytime Approaches
Ira Assent, Philipp Kranen, Corinna Baldauf and Thomas Seidl
3:20 pm - 3:40 pm
CALDS: Context-aware Learning from Data Streams
Joao Gomes, Ernestina Menasalvas and Pedro Sousa.
3:40 pm - 4:00 pm
4:00 pm - 4:20 pm
Evolutionary Clustering using Frequent Itemsets
Ravi Shankar K, G V R Kiran and Vikram Pudi
4:20 pm - 4:40 pm
Towards Subspace Clustering on Dynamic Data: An Incremental Version of PreDeCon
Hans-Peter Kriegel, Peer Kröger, Irene Ntoutsi and Arthur Zimek.
4:40 pm - 5:00 pm
Visual Analysis of News Streams with Article Threads
Milos Krstajic, Enrico Bertini, Florian Mansmann and Daniel A. Keim
5:00 pm - 5:20 pm
Conformal Prediction for Distribution-Independent On-line Learning and Anomaly Detection in Streaming Vessel Data
Rikard Laxhammar and Göran Falkman
5:20 pm
Closing Remarks
View the online workshop proceedings at the ACM Digital Library.

The best paper and a workshop report will be published in the December issue of KDD Explorations.

Margaret H. Dunham
Intelligent Data Analysis Group (IDA)
Department of Computer Science and Engineering
Lyle School of Engineering
Southern Methodist University
Dallas, Texas 75275
mhd [at]

Michael Hahsler
Intelligent Data Analysis Group (IDA)
Department of Computer Science and Engineering
Lyle School of Engineering
Southern Methodist University
Dallas, Texas 75275
mhahsler [at]

Myra Spiliopoulou
Workgroup KMD: "Knowledge Management & Discovery"
Faculty of Computer Science
Otto-von-Guericke-Universität Magdeburg
PO Box 4120, D-39016 Magdeburg
myra [at]

Program Committee
  • Sanjay Chawla, University of Sydney, Australia
  • João Gama, Universidade do Porto, Portugal
  • Le Gruenwald, NSF/University of Oklahoma, USA
  • Eamonn Keogh, University of California - Riverside, USA
  • Latifur Khan, University of Texas at Dallas, USA
  • Chi-Hoon Lee, Yahoo! Labs, USA
  • Mohammad Masud, University of Texas at Dallas, USA
  • Tamer Özsu, University of Waterloo, Canada
  • Spiros Papadimitriou, IBM T.J. Watson Research Center, USA
  • Thomas Seidl, RWTH Aachen University, Germany
  • Dimitris Tasoulis, Imperial College London, UK
Acknowledgment of Support
The organization of this workshop was supported in part by the National Science Foundation under Grant No. IIS-0948893.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Michael Hahsler <mhahsler [at]>