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005 20221102211533.0
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020 _a9780387254654
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024 7 _a10.1007/b107408
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
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072 7 _aCOM021030
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072 7 _aUNF
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072 7 _aUYQE
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082 0 4 _a006.312
_223
245 1 0 _aData Mining and Knowledge Discovery Handbook
_h[electronic resource] /
_cedited by Oded Maimon, Lior Rokach.
250 _a1st ed. 2005.
264 1 _aNew York, NY :
_bSpringer US :
_bImprint: Springer,
_c2005.
300 _aXXXVI, 1383 p. 400 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _ato Knowledge Discovery in Databases -- to Knowledge Discovery in Databases -- Preprocessing Methods -- Data Cleansing -- Handling Missing Attribute Values -- Geometric Methods for Feature Extraction and Dimensional Reduction -- Dimension Reduction and Feature Selection -- Discretization Methods -- Outlier Detection -- Supervised Methods -- to Supervised Methods -- Decision Trees -- Bayesian Networks -- Data Mining within a Regression Framework -- Support Vector Machines -- Rule Induction -- Unsupervised Methods -- Visualization and Data Mining for High Dimensional Datasets -- Clustering Methods -- Association Rules -- Frequent Set Mining -- Constraint-Based Data Mining -- Link Analysis -- Soft Computing Methods -- Evolutionary Algorithms for Data Mining -- Reinforcement-Learning: An Overview from a Data Mining Perspective -- Neural Networks -- On the Use of Fuzzy Logic in Data Mining -- Granular Computing and Rough Sets -- Supporting Methods -- 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 -- Advanced Methods -- Meta-Learning -- 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 -- Data Mining for Imbalanced Datasets: An Overview -- Relational Data Mining -- Web Mining -- A Review of Web Document Clustering Approaches -- Causal Discovery -- Ensemble Methods for Classifiers -- Decomposition Methodology for Knowledge Discovery and Data Mining -- Information Fusion -- Parallel and Grid-Based Data Mining -- Collaborative Data Mining -- Organizational Data Mining -- Mining Time Series Data -- Modelling medical diagnostic rules based on rough sets -- Data Mining in Medicine -- The statistical analysis of contingency table designs -- Learning Information Patterns in Biological Databases -- Computer Integrated Manufacturing: A Data Mining Approach -- Data Mining for Selection of Manufacturing Processes -- Learning expert systems in numerical analysis of structures -- Data Mining of Design Products and Processes -- ANSWER: Network monitoring using object-oriented rule -- Data Mining in Telecommunications -- Knowledge Discovery for Gene Regulatory Regions Analysis -- Data Mining for Financial Applications -- Data Mining for Intrusion Detection -- Data Mining for Intrusion Detection -- Fuzzy Cluster Analysis: Methods for Classification -- Data Mining for Software Testing -- Data Mining for CRM -- Data Mining for CRM -- Learning Internal Representation by Error Propagation -- Data Mining for Target Marketing -- Software -- Weka -- Oracle Data Mining -- Building Data Mining Solutions With OLE DB for DM and XML for Analysis -- LERS—A Data Mining System -- GainSmarts Data Mining System for Marketing -- Wizsoft’s Wizwhy -- DataEngine.
520 _aData 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.
650 0 _aData mining.
_93907
650 0 _aDatabase management.
_93157
650 0 _aInformation storage and retrieval systems.
_922213
650 0 _aComputer networks .
_931572
650 0 _aMultimedia systems.
_911575
650 0 _aInformation retrieval.
_910134
650 0 _aComputer architecture.
_93513
650 1 4 _aData Mining and Knowledge Discovery.
_966479
650 2 4 _aDatabase Management.
_93157
650 2 4 _aInformation Storage and Retrieval.
_923927
650 2 4 _aComputer Communication Networks.
_966480
650 2 4 _aMultimedia Information Systems.
_931575
650 2 4 _aData Storage Representation.
_931576
700 1 _aMaimon, Oded.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_93905
700 1 _aRokach, Lior.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_93909
710 2 _aSpringerLink (Online service)
_966481
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9780387505145
776 0 8 _iPrinted edition:
_z9780387244358
856 4 0 _uhttps://doi.org/10.1007/b107408
912 _aZDB-2-SCS
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