Data stream mining

Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.

In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. In many applications, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift.

Software for data stream mining

 * YALE (YALE (Yet Another Learning Environment)): free open-source software for knowledge discovery, data mining, and machine learning also featuring data stream mining, learning time-varying concepts, and tracking drifting concept (if used in combination with its data stream mining plugin (formerly: concept drift plugin))

Events on data stream mining

 * International Workshop on Knowledge Discovery from Ubiquitous Data Streams held in conjunction with the 18th European Conference on Machine Learning (ECML) and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) in Warsaw, Poland, in September 2007.
 * ACM Symposium on Applied Computing Data Streams Track held in conjunction with the 2007 ACM Symposium on Applied Computing (SAC-2007) in Seoul, Korea, in March 2007.
 * IEEE International Workshop on Mining Evolving and Streaming Data (IWMESD 2006) to be held in conjunction with the 2006 IEEE International Conference on Data Mining (ICDM-2006) in Hong Kong in December 2006.
 * Fourth International Workshop on Knowledge Discovery from Data Streams (IWKDDS) to be held in conjunction with the 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (ECML/PKDD-2006) in Berlin, Germany, in September 2006.

Researchers working on data stream mining

 * Joao Gama, University of Porto, Portugal
 * Ralf Klinkenberg, University of Dortmund, Germany
 * Mohamed Medhat Gaber, CSIRO ICT Centre, Australia
 * Olfa Nasraoui, University of Louisville, USA
 * Hua-Fu Li, National Chiao-Tung University, Taiwan
 * Eyke Hüllermeier, University of Marburg, Germany

Bibliographic references

 * Grabtree I. Soltysiak S. Identifying and Tracking Changing Interests. International Journal of Digital Libraries, Springer Verlag, vol. 2, 38-53.
 * Klinkenberg, Ralf: Learning Drifting Concepts: Example Selection vs. Example Weighting. In Intelligent Data Analysis (IDA), Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift, Vol. 8, No. 3, pages 281--300, 2004.
 * Klinkenberg, Ralf: Using Labeled and Unlabeled Data to Learn Drifting Concepts. In Kubat, Miroslav and Morik, Katharina (editors), Workshop notes of the IJCAI-01 Workshop on \em Learning from Temporal and Spatial Data, pages 16--24, IJCAI, Menlo Park, CA, USA, AAAI Press, 2001.
 * Klinkenberg, Ralf and Joachims, Thorsten: Detecting Concept Drift with Support Vector Machines. In Langley, Pat (editor), Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 487--494, San Francisco, CA, USA, Morgan Kaufmann, 2000.
 * Klinkenberg, Ralf and Renz, Ingrid: Adaptive Information Filtering: Learning in the Presence of Concept Drifts. In Sahami, Mehran and Craven, Mark and Joachims, Thorsten and McCallum, Andrew (editors), Workshop Notes of the ICML/AAAI-98 Workshop \em Learning for Text Categorization, pages 33--40, Menlo Park, CA, USA, AAAI Press, 1998.
 * Koychev I. Gradual Forgetting for Adaptation to Concept Drift. In Proceedings of ECAI 2000 Workshop Current Issues in Spatio-Temporal Reasoning. Berlin, Germany, 2000, pp. 101-106
 * Koychev I. and Schwab I., Adaptation to Drifting User’s Interests, Proc. of ECML 2000 Workshop: Machine Learning in New Information Age, Barcelona, Spain, 2000, pp. 39-45
 * Maloof, M.A. and Michalski, R.S. Learning Evolving Concepts Using Partial Memory Approach. Working Notes of the 1995 AAAI Fall Symposium on Active Learning, Boston, MA, pp. 70-73, 1995
 * Maloof M. and Michalski R. Selecting examples for partial memory learning. Machine Learning, 41(11), 2000, pp. 27-52.
 * Mitchell T., Caruana R., Freitag D., McDermott, J. and Zabowski D. Experience with a Learning Personal Assistant. Communications of the ACM 37(7), 1994, pp. 81-91.
 * Nasraoui O., Rojas C., and Cardona C., “ A Framework for Mining Evolving Trends in Web Data Streams using Dynamic Learning and Retrospective Validation ”, Journal of Computer Networks- Special Issue on Web Dynamics, 50(10), 1425-1652, July 2006
 * Nasraoui O., Cerwinske J., Rojas C., and Gonzalez F., "Collaborative Filtering in Dynamic Usage Environments", in Proc. of CIKM 2006 – Conference on Information and Knowledge Management, Arlington VA , Nov. 2006
 * Schlimmer J., and Granger R. Incremental Learning from Noisy Data, Machine Learning, 1(3), 1986, 317-357.
 * Scholz, Martin and Klinkenberg, Ralf: Boosting Classifiers for Drifting Concepts. In Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams, Vol. 11, No. 1, pages 3-28, March 2007.
 * Scholz, Martin and Klinkenberg, Ralf: An Ensemble Classifier for Drifting Concepts. In Gama, J. and Aguilar-Ruiz, J. S. (editors), Proceedings of the Second International Workshop on Knowledge Discovery in Data Streams, pages 53--64, Porto, Portugal, 2005.
 * Schwab I., Pohl W. and Koychev I. Learning to Recommend from Positive Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, 241 - 247.
 * Widmer G. Tracking Changes through Meta-Learning, Machine Learning 27, 1997, pp. 256-286.
 * Widmer G. and Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 1996, pp. 69-101.