Data Stream Mining - Bibliographic References

Bibliographic References

• Minku and Yao. "DDD: A New Ensemble Approach For Dealing With Concept Drift.", IEEE Transactions on Knowledge and Data Engineering, 24:(4), p. 619-633, 2012.
• Hahsler, Michael and Dunham, Margaret H. Temporal structure learning for clustering massive data streams in real-time. In SIAM Conference on Data Mining (SDM11), pages 664-675. SIAM, April 2011.
• Minku, White and Yao. "The Impact of Diversity on On-line Ensemble Learning in the Presence of Concept Drift.", IEEE Transactions on Knowledge and Data Engineering, 22:(5), p. 730-742, 2010.
• Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei Han, Bhavani M. Thuraisingham: Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams. ECML/PKDD (2) 2009: 79-94 (extended version will appear in TKDE journal).
• 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.
• 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
• 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
• 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.
• 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.
• Maloof M. and Michalski R. Selecting examples for partial memory learning. Machine Learning, 41(11), 2000, pp. 27–52.
• 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
• 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.
• 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
• Schwab I., Pohl W. and Koychev I. Learning to Recommend from Positive Evidence, Proceedings of Intelligent User Interfaces 2000, ACM Press, 241 - 247.
• 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.
• Grabtree I. Soltysiak S. Identifying and Tracking Changing Interests. International Journal of Digital Libraries, Springer Verlag, vol. 2, 38-53.
• Widmer G. Tracking Context Changes through Meta-Learning, Machine Learning 27, 1997, pp. 256–286.
• 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
• 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.
• Widmer G. and Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 1996, pp. 69–101.
• Schlimmer J., and Granger R. Incremental Learning from Noisy Data, Machine Learning, 1(3), 1986, 317-357.