In many domains such as infrastructure management, business process monitoring, crisis management and other monitoring activities, systems are characterised by large numbers of sensors collecting data from a variety of information sources. The information is collected in real-time and thus there is an interest for live performance analysis and reporting.
This calls for data mining methods for recognising, predicting, reasoning and controlling performance of systems. In recent years, soft computing methods and algorithms and methods have been applied to data mining, to identify patterns and new insight into data.
Three soft computing techniques were chosen, namely, Artificial Immune Systems, Bayesian Belief Networks and Neural Networks.
In this project, soft computing techniques are applied to different datasets including the WiFi network monitoring data, Kenya drought data and the pollution monitoring data. In this way, we can determine which of the techniques and algorithms work best under which circumstances.
