This workshop aims to bring together researchers and practitioners working on different aspects of machine learning, data mining and text mining in emergant 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.
• 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 health applications .
• 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 health, business, science, engineering, medicine, and other disciplines with particular attention to lessons learned.
• Visual data mining.