Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Computing Clusters of Correlation Connected objects
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CURLER: finding and visualizing nonlinear correlation clusters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Deriving quantitative models for correlation clusters
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Identification of piecewise affine systems based on statistical clustering technique
Automatica (Journal of IFAC)
Hinging hyperplane models for multiple predicted variables
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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To analyze and control a system, a model is build which describes the relationship between the inputs and the corresponding outputs. While simple systems can be described by a single linear model, more complex systems can be approximated through an assembly of linear submodels. Such piecewise affine (PWA) models consists of several convex regions and linear submodels describing the input output relationship for each such region. The more regions are considered in the PWA model, the more accurate it describes the system. Still, in real world applications, simple models are necessary for performance reasons, hence a trade-off has to be made between the model complexity and its accuracy. In this paper we discuss the employment of correlation clustering algorithms for a robust identification of PWA models with reduced complexity.