Titlebook: Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering; Larisa Angstenberger Book 2001 Springer Science+Business M

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Drucker einrichten und verwalten,cal and scientific environments, as well as advances in data storage technologies, over the last decade have lead to large amounts of data being stored in databases. Analysing and extracting valuable information from these data has become an important issue in recent research and attracted the atten

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,Ressourcen und Ereignisse überwachen,are a lot of applications in which the order of state changes of an object over time determines its membership to a certain pattern, or class. In these cases, for the correct recognition of objects it is very important not only to consider properties of objects at a certain moment in time but also t

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https://doi.org/10.1007/978-3-540-47857-7sters so that objects belonging to any one of the clusters would be as similar as possible and objects of different clusters as dissimilar as possible. The most important problem arising in this context is the choice of a relevant similarity measure, which is then used for the definition of the clus

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https://doi.org/10.1007/978-3-540-47857-7nsidered in this chapter. The first example taken from credit industry and presented in Section 6.1 is concerned with the problem of bank customer segmentation based on customers’ behavioural data. After a description of the credit data of bank customers and the formulation of the goals of the analy

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Schnelle numerische lineare Algebrage number of applications where for a correct recognition of structure in data the consideration of the temporal development of objects over time is required, for instance state-dependent machine maintenance and diagnosis, the analysis of bank customers’ behaviour for the evaluation of their creditw

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Book 2001rful in pattern recognition andconsiders the entire process of dynamic pattern recognition. This booksets a general framework for Dynamic Pattern Recognition, describingin detail the monitoring process using fuzzy tools and the adaptationprocess in which the classifiers have to be adapted, using the