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The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In(More)
The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In(More)
Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. Here we report that highly irregular firing can be induced through a combination of NMDA- and(More)
Stream data mining is the process of excerpting knowledge structure from large, continuous data. For stream data, various techniques are proposed for preparing the data for data mining task. In recent years stream data have become a growing area for the researcher, but there are many issues occurring in classifying these data due to erroneous and noisy(More)
— This paper presents an approach for visualizing high-dimensional fuzzy rules arranged in a hierarchy together with the training patterns they cover. A standard multi-dimensional scaling method is used to map the rule centers of the top hierarchy level to one coherent picture. Rules of the underlying levels are projected relatively to their parent(More)
— This paper presents an approach to visualizing and exploring high-dimensional rules in two-dimensional views. The proposed method uses multi-dimensional scaling to place the rule centers and subsequently extends the rules' regions to depict their overlap. This results not only in a visualization of the rules' distribution but also enables the relationship(More)
In Mixed Fuzzy Rule Formation [Int. J. Approx. Reason. 32 (2003) 67] a method to extract mixed fuzzy rules from data was introduced. The underlying algorithm's performance is influenced by the choice of fuzzy t-norm and t-conorm, and a heuristic to avoid conflicts between patterns and rules of different classes throughout training. In the following addendum(More)
In this paper we describe the open source data analytics platform KNIME, focusing particularly on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms , rule induction methods, and interactive clustering tools. In addition we outline a number of experimental extensions, which are not yet part of the(More)