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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing (163)) 🔍
Ashish Ghosh (auth.), Dr. Ashish Ghosh, Prof. Lakhmi C. Jain (eds.) Springer Berlin, Studies in fuzziness and soft computing, Vol. 163, Berlin Heidelberg New York, 2005
英语 [en] · PDF · 16.4MB · 2005 · 📘 非小说类图书 · 🚀/duxiu/lgli/lgrs/scihub · Save
描述
Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).
Erscheinungsdatum: 18.10.2004
备用文件名
scihub/10.1007/978-3-540-32358-7.pdf
备选作者
Ashish Ghosh, Lakhmi C.Jain,Springer
备选作者
Ashish Ghosh (editor)
备用出版商
Springer Spektrum. in Springer-Verlag GmbH
备用出版商
Steinkopff. in Springer-Verlag GmbH
备用版本
Studies in fuzziness and soft computing,, v. 163, Berlin, New York, Germany, 2005
备用版本
Springer Nature, Berlin, 2005
备用版本
Germany, Germany
备用版本
2005, PS, 2004
元数据中的注释
sm36767461
元数据中的注释
Includes bibliographical references and index.
备用描述
This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. "Evolutionary Computation in Data Mining" provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics. TOC:Evolutionary Algorithms for Data Mining and Knowledge Discovery.- Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining.- GAP: Constructing and Selecting Features with Evolutionary Computing.- Multi-Agent Data Mining using Evolutionary Computing.- A Rule Extraction System with Class-Dependent Features.- Knowledge Discovery in Data Mining via an Evolutionary Algorithm.- Diversity and Neuro-Ensemble.- Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets.- Evolutionary Computation in Intelligent Network Management.- Genetic Programming in Data Mining for Drug Discovery.- Microarray Data Mining with Evolutionary Computation.- An Evolutionary Modularized Data Mining Mechanism for Financial Distress Forecasts
备用描述
<p><p>this Carefully Edited Book Reflects And Advances The State Of The Art In The Area Of Data Mining And Knowledge Discovery With Evolutionary Algorithms. It Emphasizes The Utility Of Different Evolutionary Computing Tools To Various Facets Of Knowledge Discovery From Databases, Ranging From Theoretical Analysis To Real-life Applications. Evolutionary Computation In Data Mining Provides A Balanced Mixture Of Theory, Algorithms And Applications In A Cohesive Manner, And Demonstrates How The Different Tools Of Evolutionary Computation Can Be Used For Solving Real-life Problems In Data Mining And Bioinformatics.</p>
备用描述
"This book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. "Evolutionary Computation in Data Mining" provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics."--BOOK JACKET
开源日期
2015-01-29
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