This book offers a panoramic view of recent advances in spatial complexity, in order to enhance our understanding of complex spatial networks by simplicity in terms of both the basic driving forces of systemic impacts and the modelling of such systems. Simple models mapping out the evolution of...

This book deals with knowledge discovery and data mining in spatial and temporal data, seeking to present novel methods that can be employed to discover spatial structures and processes in complex data. Spatial knowledge discovery is examined through the tasks of clustering, classification,...

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has...

In critical care extremely high dimensional time series are generated by clinical information systems. This yields new perspectives of data recording and also causes a new challenge for statistical methodology. Recently graphical correlation models have been developed for analysing the partial...

In critical care extremely high dimensional time series are generated by clinical information systems. This yields new perspectives of data recording and also causes a new challenge for statistical methodology. Recently graphical correlation models have been developed for analysing the partial...