Communications of the ACM - Special issue on parallelism
Instance-Based Learning Algorithms
Machine Learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Adapting to Drift in Continuous Domains (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Estimating Rarity and Similarity over Data Stream Windows
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in data stream management
ACM SIGMOD Record
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
ACM SIGMOD Record
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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The processing of data streams in general and the mining of such streams in particular have recently attracted considerable attention in various research fields. A key problem in stream mining is to extend existing machine learning and data mining methods so as to meet the increased requirements imposed by the data stream scenario, including the ability to analyze incoming data in an online, incremental manner, to observe tight time and memory constraints, and to appropriately respond to changes of the data characteristics and underlying distributions, amongst others. This paper considers the problem of classification on data streams and develops an instance-based learning algorithm for that purpose. The experimental studies presented in the paper suggest that this algorithm has a number of desirable properties that are not, at least not as a whole, shared by currently existing alternatives. Notably, our method is very flexible and thus able to adapt to an evolving environment quickly, a point of utmost importance in the data stream context. At the same time, the algorithm is relatively robust and thus applicable to streams with different characteristics.