Last edited by Akilrajas
Saturday, May 9, 2020 | History

7 edition of Data Mining and Knowledge Discovery with Evolutionary Algorithms found in the catalog.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

by Alex A. Freitas

  • 338 Want to read
  • 5 Currently reading

Published by Springer .
Written in English

    Subjects:
  • Databases & data structures,
  • Information Storage & Retrieval,
  • Data mining,
  • Programming - General,
  • Algorithms (Computer Programming),
  • Database Engineering,
  • Computers,
  • Computers - Other Applications,
  • Database searching,
  • Computer Books: General,
  • Database Management - General,
  • Artificial Intelligence - General,
  • Database Management - Database Mining,
  • Programming - Algorithms,
  • Artificial Intelligence,
  • Computers / Artificial Intelligence,
  • Computers : Database Management - Database Mining,
  • Computers : Programming - Algorithms,
  • Computing Methodologies,
  • Evolutionary Algorithms,
  • Computer algorithms

  • The Physical Object
    FormatHardcover
    Number of Pages279
    ID Numbers
    Open LibraryOL9057661M
    ISBN 103540433317
    ISBN 109783540433316

    Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common. Some of applications of evolutionary algorithms in data mining, which involves human interaction, are presented in this paper. When dealing with concepts that are abstract and hard to define or cases where there are a large or variable number of parameters, we still .

    Neural Network Methods.- to Soft Computing for Knowledge Discovery and Data Mining.- Neural Networks For Data Mining.- Improved SOM Labeling Methodology for Data Mining Applications.- Evolutionary Methods.- A Review of evolutionary Algorithms for Data Mining.- Genetic Clustering for . This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets.

    Knowledge Management & Knowledge-based Systems, Knowledge Discovery and Data Mining, Logic and Logic Programming, Machine Learning, Evolutionary Algorithms & Neural Networks, Natural Language and Speech Processing, AI Applications. (). Data Mining and Knowledge Discovery with Evolutionary Algorithms by Alex A. Freitas English | PDF | | Pages | ISBN: | MB This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research. In general.


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Data Mining and Knowledge Discovery with Evolutionary Algorithms by Alex A. Freitas Download PDF EPUB FB2

The motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. This book emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision by: Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.

This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses.

We show how the requirements of data mining and knowledge discovery influence the design of evolutionary algorithms. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from by: "In the snappily-titled Data Mining and Knowledge Discovery with Evolutionary Algorithms, leading researcher Alex A Freitas introduces both data mining and evolutionary algorithms.

The aim is to introduce and address the key challenges to a high level of : Springer-Verlag Berlin Heidelberg. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the.

Data Mining and Knowledge Discovery with Evolutionary Algorithms by Alex A. Freitas,available at Book Depository with free delivery worldwide/5(4). Data Mining and Knowledge Discovery with Evolutionary Algorithms [Freitas Alex A.] on *FREE* shipping on qualifying offers.

Brand New5/5(1). This book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas ingly popular in the last few years, and their integration is currently an area of active research.

In essence, data mining consists of extracting valid, comprehensible, and in teresting knowledge from data/5(2). Get this from a library. Data mining and knowledge discovery with evolutionary algorithms. [Alex A Freitas] -- "This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas.

It is. This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area.

In general, data mining consists of extracting knowledge from by: Data Mining and Knowledge Discovery Handbook, 2nd ed book covers in a succinct and orderly manner the methods one needs to master in. 19 A Review of Evolutionary Algorithms for Data Mining. Evolutionary data mining, or genetic data mining is an umbrella term for any data mining using evolutionary it can be used for mining data from DNA sequences, it is not limited to biological contexts and can be used in any classification-based prediction scenario, which helps "predict the value of a user-specified goal attribute based on the values of other attributes.".

Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery, and databases as a toolbox of relevant tools.

data mining and knowledge discovery with evolutionary algorithms data mining consists of extracting knowledge from data. In this book we particularly emphasise the importance of discovering comprehensible and interesting knowledge, which is potentially useful to the reader for intelligent decision making.

the motivation for applying. The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD).

These articles are written by leading experts around the : Springer-Verlag Berlin Heidelberg. From the reviews:"In the snappily-titled Data Mining and Knowledge Discovery with Evolutionary Algorithms, leading researcher Alex A Freitas introduces both data mining and evolutionary algorithms.

' The aim is to introduce and address the key challenges to a high level of detail. A Knowledge Discovery Approach. Author: Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan. Publisher: Springer Science & Business Media ISBN: Category: Computers Page: View: DOWNLOAD NOW» This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in.

Abstract: This chapter discusses the use of evolutionary algorithms, particularly genetic algorithms and genetic programming, in data mining and knowledge discovery.

We focus on the data mining task of classification. In addition, we discuss some preprocessing and. From the editors of the leading Data Mining and Knowledge Discovery Handbook,this volume, by highly regarded authors, includes selected contributors of the Handbook.

The first three parts of this book are devoted to the principal constituents of soft computing: neural networks, evolutionary algorithms and. Daniel T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, ISBN:John Wiley, (see also companion site for Larose book). Gary Miner, John Elder IV, Thomas Hill, Robert Nisbet, Dursun Delen, Andrew Fast, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, Academic Press.

Data Mining and Knowledge Discovery is a triannual peer-reviewed scientific journal focusing on data mining. It is published by Springer Science+Business Media.

As ofthe editor-in-chief is Geoffrey I. Webb. It was started in and launched in by Usama Fayyad as founding Editor-in-Chief by Kluwer Academic Publishers (later becoming Springer).This book constitutes the refereed proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '97, held in Trondheim, Norway, in June The volume presents a total of 38 revised full papers together with abstracts of one invited talk and four tutorials.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.