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.

The main topics to be addressed include (but not limited to):

• 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.