Data stream mining

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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. Often, concepts from the field of incremental learning, a generalization of Incremental heuristic search are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, 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[edit]

  • MOA (Massive Online Analysis): free open-source software specific for mining data streams with concept drift. It has several machine learning algorithms (classification, regression, clustering, outlier detection and recommender systems). Also it contains a prequential evaluation method, the EDDM concept drift methods, a reader of ARFF real datasets, and artificial stream generators as SEA concepts, STAGGER, rotating hyperplane, random tree, and random radius based functions. MOA supports bi-directional interaction with Weka (machine learning).
  • RapidMiner: commercial 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))


Master References[edit]

Bibliographic References[edit]

  • Rutkowski, Jaworski, Pietruczuk and Duda: "A New Method for Data Stream Mining Based on the Misclassification Error", IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 5, pp. 1048-1059, 2015.
  • Shaker, Ammar and Lughofer, Edwin. "Self-Adaptive and Local Strategies for a Smooth Treament of Drifts in Data Streams.", Evolving Systems, 5:(4), p. 239-257, 2014.
  • Rutkowski, Pietruczuk, Duda, and Jaworski: "Decision Trees for Mining Data Streams Based on the McDiarmid's Bound", IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1272-1279, 2013.
  • 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.
  • Lughofer, Edwin and Angelov, Plamen. "Handling Drifts and Shifts in On-line Data Streams with Evolving Fuzzy Systems.", Applied Soft Computing, 11:(2), p. 2057-2068, 2011.
  • 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.


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