000 04431nam a22005175i 4500
001 978-0-387-36795-8
003 DE-He213
005 20250710083956.0
007 cr nn 008mamaa
008 100301s2007 xxu| s |||| 0|eng d
020 _a9780387367958
_a99780387367958
024 7 _a10.1007/978-0-387-36795-8
_2doi
082 0 4 _a006.312
_223
100 1 _aCios, Krzysztof J.
_eauthor.
245 1 0 _aData Mining
_h[recurso electrónico] :
_bA Knowledge Discovery Approach /
_cby Krzysztof J. Cios, Roman W. Swiniarski, Witold Pedrycz, Lukasz A. Kurgan.
264 1 _aBoston, MA :
_bSpringer US,
_c2007.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aData Mining and Knowledge Discovery Process -- The Knowledge Discovery Process -- Data Understanding -- Data -- Concepts of Learning, Classification, and Regression -- Knowledge Representation -- Data Preprocessing -- Databases, Data Warehouses, and OLAP -- Feature Extraction and Selection Methods -- Discretization Methods -- Data Mining: Methods for Constructing Data Models -- Unsupervised Learning: Clustering -- Unsupervised Learning: Association Rules -- Supervised Learning: Statistical Methods -- Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids -- Supervised Learning: Neural Networks -- Text Mining -- Data Models Assessment -- Assessment of Data Models -- Data Security and Privacy Issues -- Data Security, Privacy and Data Mining.
520 _aThis comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects. Based upon the authors' previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers: A suite of exercises at the end of every chapter, designed to enhance the reader's understanding of the theory and proficiency with the tools presented Links to all-inclusive instructional presentations for each chapter to ensure easy use in classroom teaching Extensive appendices covering relevant mathematical material for convenient look-up Methods for addressing issues related to data privacy and security within the context of data mining, enabling the reader to balance potentially conflicting aims Summaries and bibliographical notes for each chapter, providing a broader perspective of the concepts and methods described Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects.
650 0 _aCOMPUTER SCIENCE.
650 0 _aDATA MINING.
650 0 _aINFORMATION STORAGE AND RETRIEVAL SYSTEMS.
650 0 _aARTIFICIAL INTELLIGENCE.
650 0 _aOPTICAL PATTERN RECOGNITION.
650 0 _aSTATISTICS.
650 1 4 _aCOMPUTER SCIENCE.
650 2 4 _aDATA MINING AND KNOWLEDGE DISCOVERY.
650 2 4 _aINFORMATION STORAGE AND RETRIEVAL.
650 2 4 _aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
650 2 4 _aPATTERN RECOGNITION.
650 2 4 _aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
700 1 _aSwiniarski, Roman W.
_eauthor.
700 1 _aPedrycz, Witold.
_eauthor.
700 1 _aKurgan, Lukasz A.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387333335
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-387-36795-8
_zVer el texto completo en las instalaciones del CICY
912 _aZDB-2-SCS
942 _2ddc
_cER
999 _c57523
_d57523