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Data Mining with Computational Intelligence

By: Wang, Lipo [author.].
Contributor(s): Fu, Xiuju [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advanced Information and Knowledge Processing: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.Description: XI, 276 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540288039.Subject(s): Computer science | Database management | Data mining | Artificial intelligence | Computer Science | Data Mining and Knowledge Discovery | Database Management | Artificial Intelligence (incl. Robotics)DDC classification: 006.312 Online resources: Click here to access online
Contents:
MLP Neural Networks for Time-Series Prediction and Classification -- Fuzzy Neural Networks for Bioinformatics -- An Improved RBF Neural Network Classifier -- Attribute Importance Ranking for Data Dimensionality Reduction -- Genetic Algorithms for Class-Dependent Feature Selection -- Rule Extraction from RBF Neural Networks -- A Hybrid Neural Network For Protein Secondary Structure Prediction -- Support Vector Machines for Prediction -- Rule Extraction from Support Vector Machines.
In: Springer eBooksSummary: Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.
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E books E books PK Kelkar Library, IIT Kanpur
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MLP Neural Networks for Time-Series Prediction and Classification -- Fuzzy Neural Networks for Bioinformatics -- An Improved RBF Neural Network Classifier -- Attribute Importance Ranking for Data Dimensionality Reduction -- Genetic Algorithms for Class-Dependent Feature Selection -- Rule Extraction from RBF Neural Networks -- A Hybrid Neural Network For Protein Secondary Structure Prediction -- Support Vector Machines for Prediction -- Rule Extraction from Support Vector Machines.

Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.

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