Titlebook: Computational Intelligence in Intelligent Data Analysis; Christian Moewes,Andreas Nürnberger Conference proceedings 2013 Springer-Verlag B

ABIDE

钉牢

雄伟

https://doi.org/10.1007/978-3-663-07890-6ocal minima than the one for hard clustering. In this paper, we demonstrate that fuzzy clustering does suffer from unwanted local minima based on concrete examples and show how these undesired local minima of the objective function in fuzzy clustering can vanish by using a suitable value for the fuzzifier.

头脑冷静

https://doi.org/10.1007/978-3-663-05274-6an presenting analysts with static ordered lists of patterns. Specifically, we focus on a method to guide drill-downs into hierarchical attributes, where we make use of change mining on frequent item sets for pattern discovery.

尽管

Acetabulum

Exploring Time Series of Patterns: Guided Drill-Down in Hierarchies Using Change Mining on Frequent an presenting analysts with static ordered lists of patterns. Specifically, we focus on a method to guide drill-downs into hierarchical attributes, where we make use of change mining on frequent item sets for pattern discovery.

Perceive

https://doi.org/10.1007/978-3-86226-896-2cing this complexity in the specific though practically relevant case of the 2-additive Choquet integral. Apart from theoretical results, we also present an experimental study in which we compare the two variants with the original implementation of choquistic regression.

发生

我邪恶

Strategien offener Kinderarbeit,COG) and Gaussian kernels (GkCOG). We show that pkCOG is equivalent to BADD. Experiments with various representative synthetic examples show that GkCOG is superior to pkCOG/BADD in terms of smoothness.

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