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- W2749716760 abstract "The present thesis deals with the technological problem of predicting fil-tration from an information technological point of view, by application of data mining methods. This work follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) approach and base upon the analysis of the historic data (production and laboratory data of five years) of a Munich brewery. Within the DM process three sub tasks are carried out. Firstly, the filtra-tion are classified and evaluated by means of two approaches, differing by the type of data reduction. The resulting filtration classes, e.g. “good” or “poor”, are validated by the use of experts knowledge, showing the cor-rectness of the automatically revealed classes and their evaluation. This shows the aptitude of the applied methods. The second task is given by the analysis of the models input data, thus the laboratory data. This tasks aims at findings patterns within data. The results of these two tasks form the fundament for the third task, the prediction of filtration. Within this work the following methods are used: Cluster Analysis, Deci-sion Trees, Artificial Neural Networks, Fuzzy Logic, Expectation Maximisa-tion and Principal Components Analysis. The results and findings are vali-dated by splitting the data in two samples, one for training and one for evaluation, as well as by experts knowledge. Finally, this thesis demonstrates the aptitude of DM methods for analysis and knowledge discovery in the field of brewing technology. The prediction of filtration yields prediction rates of about 80%, depending on the applied modelling technique and data reduction. The variation of the different methods and types of data reduction indicates a lack of information. A fur-ther increase of prediction quality can not be enhanced by means of methods but by an extension of input data." @default.
- W2749716760 created "2017-08-31" @default.
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- W2749716760 date "2006-01-01" @default.
- W2749716760 modified "2023-09-24" @default.
- W2749716760 title "Optimisation of Filtration by Application of Data Mining Methods" @default.
- W2749716760 hasPublicationYear "2006" @default.
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