Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Accuracy updated ensemble for data streams with concept drift
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Summarizing cluster evolution in dynamic environments
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Batch weighted ensemble for mining data streams with concept drift
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Space-time roll-up and drill-down into geo-trend stream cubes
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Time stamping in the presence of latency and drift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
New drift detection method for data streams
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Online evaluation of email streaming classifiers using GNUsmail
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
iMMPC: a local search approach for incremental Bayesian network structure learning
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Discovery and diagnosis of behavioral transitions in patient event streams
ACM Transactions on Management Information Systems (TMIS)
Thermal modeling of power transformers using evolving fuzzy systems
Engineering Applications of Artificial Intelligence
Dynamic detection of nuclear reactor core incident
Signal Processing
Editorial: Editorial of the special issue: Online fuzzy machine learning and data mining
Information Sciences: an International Journal
Handling time changing data with adaptive very fast decision rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
Novelty detection algorithm for data streams multi-class problems
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Mining big data: current status, and forecast to the future
ACM SIGKDD Explorations Newsletter
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Knowledge Discovery in Higher Educational Big Dataset
International Journal of Information Retrieval Research
Editorial: Special Issue: Evolving Soft Computing Techniques and Applications
Applied Soft Computing
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.