data mining and knowledge discovery handbook pdf

Data mining and knowledge discovery handbook pdf

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Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook

Maimon, Oded

Kundrecensioner

Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining DM and knowledge discovery in databases KDD into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently.

This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering.

This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. Skip to main content Skip to table of contents. Advertisement Hide. This service is more advanced with JavaScript available.

Data Mining and Knowledge Discovery Handbook. Front Matter Pages i-xxxv. Introduction to Knowledge Discovery in Databases. Pages Data Cleansing.

Handling Missing Attribute Values. Jerzy W. Grzymala-Busse, Witold J. Dimension Reduction and Feature Selection. Discretization Methods. Ying Yang, Geoffrey I. Webb, Xindong Wu. Outlier Detection. Introduction to Supervised Methods. Decision Trees. Bayesian Networks.

Paola Sebastiani, Maria M. Abad, Marco F. Data Mining within a Regression Framework. Support Vector Machines. Rule Induction. Clustering Methods. Association Rules. Frequent Set Mining. Constraint-Based Data Mining. Jean-Francois Boulicaut, Baptiste Jeudy. Link Analysis. Evolutionary Algorithms for Data Mining. Neural Networks. Granular Computing and Rough Sets. Statistical Methods for Data Mining. Logics for Data Mining. Wavelet Methods in Data Mining. Fractal Mining. Interesting Measures.

Quality Assessment Approaches in Data Mining. Data Mining Model Comparison. Data Mining Query Languages. Jean-Francois Boulicaut, Cyrille Masson. Bias vs Variance Decomposition for Regression and Classification.

Mining with Rare Cases. Mining Data Streams. Mining High-Dimensional Data. Text Mining and Information Extraction. Spatial Data Mining. Relational Data Mining. Web Mining. Nora Oikonomakou, Michalis Vazirgiannis. Causal Discovery. Hong Yao, Cory J. Butz, Howard J. Ensemble Methods for Classifiers. Information Fusion. Parallel and Grid-Based Data Mining. Collaborative Data Mining. About this book Introduction Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining DM and knowledge discovery in databases KDD into a coherent and unified repository.

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Data Mining and Knowledge Discovery Handbook

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining DM and knowledge discovery in databases KDD into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. Skip to main content Skip to table of contents.

Maimon, Oded

It seems that you're in Germany. We have a dedicated site for Germany. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology.

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This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries. Contributors are drawn from noted academic institutions and companies around the world and across diverse disciplines.

Kundrecensioner

Embed Size px x x x x Industrial Engineering Ramat AvivIsraelmaimon eng. Ben-Gurion University of the NegevDept. Use inconnection with any form of information storage and retrieval, electronic adaptation, computersoftware, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even ifthey are not identied as such, is not to be taken as an expression of opinion as to whether or notthey are subject to proprietary rights. Printed on acid-free paper.

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining DM and knowledge discovery in databases KDD into a coherent and unified repository.

Weka: A machine learning workbench for data mining

Burges as radial basis function kernels , this centering is equivalent to centering a distance matrix in feature space. Williams, further points out that for these kernels, classical MDS in feature space is equivalent to a form of metric MDS in input space. The subject of feature extraction and dimensional reduction is vast. Acknowledgments I thank John Platt for valuable discussions. References M. Aizerman, E. Braverman, and L.

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Data Mining And Knowledge Discovery Handbook

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2 comments

  • Morgana L. 31.05.2021 at 13:29

    Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges.

    Reply
  • Fabrice S. 05.06.2021 at 10:29

    Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining.

    Reply

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