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The partitioning of a time-series into internally homogeneous segments is an important data mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore it is not practical to define crisp bounds of the segments. Although fuzzy clustering algorithms are widely used to(More)
The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster must come from successive time points. The changes of the variables of a time-series are usually vague and do not focused on any particular time point. Therefore it is not practical(More)
Time-series segmentation algorithms, such as methods based on Principal Component Analysis (PCA) and fuzzy clustering, are based on input-output process data. However, historical process data alone may not be sufficient for the monitoring of process transitions. Hence, the key idea of this paper is to incorporate the first-principle model based state(More)
Nonlinear state estimation is a useful approach to the monitoring of industrial (polymerization) processes. This paper investigates how this approach can be followed to the development of a soft sensor of the product quality (melt index). The bottleneck of the successful application of advanced state estimation algorithms is the identification of models(More)
The huge amount of data recorded by modern production systems definitely have the potential to provide information for product and process design, monitoring and control. This paper presents a soft-computing based approach for the extraction of knowledge from the historical data of production. Since Self-Organizing Maps (SOM) provide compact representation(More)
Selecting the embedding dimension of a dynamic system is a key step toward the analysis and prediction of nonlinear and chaotic time-series. This paper proposes a clustering-based algorithm for this purpose. The clustering is applied in the reconstructed space defined by the lagged output variables. The intrinsic dimension of the reconstructed space is then(More)
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