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A statistical learning method to fast generalised rule induction directly from raw measurements

Le, T., Stahl, F., Wrench, C. and Gaber, M. M. (2017) A statistical learning method to fast generalised rule induction directly from raw measurements. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 18-20 Dec 2016, Anaheim, California, USA, pp. 935-938.

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Induction of descriptive models is one of the most important technologies in data mining. The expressiveness of descriptive models are of paramount importance in applications that examine the causality of relationships between variables. Most of the work on descriptive models has concentrated on less expressive approaches such as clustering algorithms or rule-based approaches that are limited to a particular type of data, such as association rule mining for binary data. However, in many applications its important to understand the structure of the produced model for further human evaluation. In this research we present a novel generalised rule induction method that allows the induction of descriptive and expressive rules directly from both categorical and numerical features.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:69929
Uncontrolled Keywords:data mining;learning (artificial intelligence);statistical analysis;association rule mining;binary data;clustering algorithms;data mining;descriptive models;expressive rules;fast generalised rule induction method;human evaluation;raw measurements;statistical learning method;Automobiles;Clustering algorithms;Data models;Gaussian distribution;Magnetic heads;Numerical models;Training data;Descriptive Data Mining;Expressive Rule Induction;Generalised Rules


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