The 3rd International Workshop on Machine Learning and Data Mining for Sensor Networks (MLDM-SN)
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When : 2016-05-23
Where : Madrid, Spain
Submission Deadline : 2016-02-28
Categories : Networks & Communications , Software Engineering & Security
The 3rd International Workshop on Machine Learning and Data Mining for Sensor Networks
(MLDM-SN)
May 23-26, 2016
Madrid, Spain
Call For Papers
This workshop aims to bring together researchers and practitioners working on different aspects of machine learning, data mining and sensor networks technologies in an effort to highlight the state-of-the-art and discuss the challenges and opportunities to explore new research directions. .
Topics
- Software agents approaches.
- Data mining processes including data selection, sampling, cleaning, reduction, transformation, integration and aggregation, as well as model development, validation and deployment.
- Data mining approaches to overcome sensor limitations such as available energy for transmission, computational power, memory, and communications bandwidth.
- Distributed Bayesian learning (belief networks, decision networks)
- Distributed clustering methods (distributed k-Means, dynamic neural networks)
- Distributed machine learning (neural networks, support vector machines, decisions trees and rules, genetic algorithms) in sensor networks
- Distributed Principal Component Analysis (PCA) and Independent Component Analysis (ICA)
- Distributed statistical regression methods in sensor networks.
- Efficient, scalable and distributed algorithms for large-scale DDM tasks such as classification, prediction, link analysis, time series analysis, clustering, and anomaly detection.
- Incremental, exploratory and interactive mining.
- Mining of data streams.
- Power consumption characteristics of distributed data mining algorithms and developing data mining algorithms to minimize power consumption.
- Privacy sensitive data mining.
- Applications of data mining for senor networks in business, science, engineering, medicine, and other disciplines with particular attention to lessons learned.
- Theoretical foundations in data mining and sensor network; extensions of computational learning theory to sensor networks.
- Visual data mining.