Machine Learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
Machine Learning
Similarity measures, author cocitation analysis, and information theory: Brief Communication
Journal of the American Society for Information Science and Technology
Hierarchical Clustering of Time-Series Data Streams
IEEE Transactions on Knowledge and Data Engineering
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
Clustering distributed sensor data streams using local processing and reduced communication
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
L2GClust: local-to-global clustering of stream sources
Proceedings of the 2011 ACM Symposium on Applied Computing
A simple dense pixel visualization for mobile sensor data mining
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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Sensors distributed all around electrical-power distribution networks produce streams of data at high-speed. From a data mining perspective, this sensor network problem is characterized by a large number of variables (sensors), producing a continuous flow of data, in a dynamic non-stationary environment. Companies make decisions to buy or sell energy based on load profiles and forecast. In this work we analyze the most relevant data mining problems and issues: continuously learning clusters and predictive models, model adaptation in large domains, and change detection and adaptation. The goal is to continuously maintain a clustering model, defining profiles, and a predictive model able to incorporate new information at the speed data arrives, detecting changes and adapting the decision models to the most recent information. We present experimental results in a large real-world scenario, illustrating the advantages of the continuous learning and its competitiveness against Wavelets based prediction. We also propose a light electrical load visualization system which enhances the ability to inspect forecast results in mobile devices.