Titlebook: Data Mining and Computational Intelligence; Abraham Kandel,Mark Last,Horst Bunke Book 2001 Physica-Verlag Heidelberg 2001 computational in

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Maria Pia De Padova,Antonella Tostil research areas such as statistics, artificial intelligence, machine learning, and soft computing have contributed to the arsenal of methods for data mining. In this paper, however, we focus on neuro-fuzzy methods for rule learning. In our opinion, fuzzy approaches can play an important role in dat

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https://doi.org/10.1007/3-540-30223-9echnique for the mining of such rules in databases containing both types of data. This technique, which we call Fuzzy Miner, performs its tasks by the use of fuzzy logic, a set of transformation functions, and by residual analysis. With the transformation functions, new attributes and new item types

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Maria Pia De Padova,Antonella Tostifor data-based rule generation has been demonstrated impressively in numerous real-world tasks. However, there are still difficulties in generating small interpretable rule bases efficiently, especially for applications with many input variables. The Fuzzy-ROSA method presented here was developed to

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J. Lakatos,K. K?ll?,G. Skaliczki,G. Holnapy practitioners. Many efficient algorithms have been proposed in the literature, e.g., Apriori, Partition, DIC, for mining association rules in the context of marketbasket analysis. They are all based on apriori methods, i.e., pruning the itemset lattice, and requires multiple database accesses. Howe

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Metabolic and Endocrine Diseases,ning Technique (DPT1) generates a classifier model by the use of Single Attribute Partitioning Method and neural network training. Single Attribute Partitioning Technique partitions a single input dimension at a time using proportional analysis. The second Dimensional Partitioning Technique (DPT2),

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