Data mining : (Record no. 59804)

000 -LEADER
fixed length control field 13428nam a2201369 i 4500
001 - CONTROL NUMBER
control field 6105606
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421114237.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 110203t20152011njua ob 001 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781118029145
-- oBook ISBN
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- cloth
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- cloth
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- ePDF ISBN
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- ePub ISBN
082 00 - CLASSIFICATION NUMBER
Call Number 006.3/12
100 1# - AUTHOR NAME
Author Kantardzic, Mehmed.
245 10 - TITLE STATEMENT
Title Data mining :
Sub Title concepts, models, methods, and algorithms /
250 ## - EDITION STATEMENT
Edition statement 2nd ed.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (xvii, 534 pages) :
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface to the Second Edition xiii -- Preface to the First Edition xv -- 1 DATA-MINING CONCEPTS 1 -- 1.1 Introduction 1 -- 1.2 Data-Mining Roots 4 -- 1.3 Data-Mining Process 6 -- 1.4 Large Data Sets 9 -- 1.5 Data Warehouses for Data Mining 14 -- 1.6 Business Aspects of Data Mining: Why a Data-Mining Project Fails 17 -- 1.7 Organization of This Book 21 -- 1.8 Review Questions and Problems 23 -- 1.9 References for Further Study 24 -- 2 PREPARING THE DATA 26 -- 2.1 Representation of Raw Data 26 -- 2.2 Characteristics of Raw Data 31 -- 2.3 Transformation of Raw Data 33 -- 2.4 Missing Data 36 -- 2.5 Time-Dependent Data 37 -- 2.6 Outlier Analysis 41 -- 2.7 Review Questions and Problems 48 -- 2.8 References for Further Study 51 -- 3 DATA REDUCTION 53 -- 3.1 Dimensions of Large Data Sets 54 -- 3.2 Feature Reduction 56 -- 3.3 Relief Algorithm 66 -- 3.4 Entropy Measure for Ranking Features 68 -- 3.5 PCA 70 -- 3.6 Value Reduction 73 -- 3.7 Feature Discretization: ChiMerge Technique 77 -- 3.8 Case Reduction 80 -- 3.9 Review Questions and Problems 83 -- 3.10 References for Further Study 85 -- 4 LEARNING FROM DATA 87 -- 4.1 Learning Machine 89 -- 4.2 SLT 93 -- 4.3 Types of Learning Methods 99 -- 4.4 Common Learning Tasks 101 -- 4.5 SVMs 105 -- 4.6 kNN: Nearest Neighbor Classifi er 118 -- 4.7 Model Selection versus Generalization 122 -- 4.8 Model Estimation 126 -- 4.9 90% Accuracy: Now What? 132 -- 4.10 Review Questions and Problems 136 -- 4.11 References for Further Study 138 -- 5 STATISTICAL METHODS 140 -- 5.1 Statistical Inference 141 -- 5.2 Assessing Differences in Data Sets 143 -- 5.3 Bayesian Inference 146 -- 5.4 Predictive Regression 149 -- 5.5 ANOVA 155 -- 5.6 Logistic Regression 157 -- 5.7 Log-Linear Models 158 -- 5.8 LDA 162 -- 5.9 Review Questions and Problems 164 -- 5.10 References for Further Study 167 -- 6 DECISION TREES AND DECISION RULES 169 -- 6.1 Decision Trees 171 -- 6.2 C4.5 Algorithm: Generating a Decision Tree 173 -- 6.3 Unknown Attribute Values 180 -- 6.4 Pruning Decision Trees 184.
505 8# - FORMATTED CONTENTS NOTE
Remark 2 6.5 C4.5 Algorithm: Generating Decision Rules 185 -- 6.6 CART Algorithm & Gini Index 189 -- 6.7 Limitations of Decision Trees and Decision Rules 192 -- 6.8 Review Questions and Problems 194 -- 6.9 References for Further Study 198 -- 7 ARTIFICIAL NEURAL NETWORKS 199 -- 7.1 Model of an Artifi cial Neuron 201 -- 7.2 Architectures of ANNs 205 -- 7.3 Learning Process 207 -- 7.4 Learning Tasks Using ANNs 210 -- 7.5 Multilayer Perceptrons (MLPs) 213 -- 7.6 Competitive Networks and Competitive Learning 221 -- 7.7 SOMs 225 -- 7.8 Review Questions and Problems 231 -- 7.9 References for Further Study 233 -- 8 ENSEMBLE LEARNING 235 -- 8.1 Ensemble-Learning Methodologies 236 -- 8.2 Combination Schemes for Multiple Learners 240 -- 8.3 Bagging and Boosting 241 -- 8.4 AdaBoost 243 -- 8.5 Review Questions and Problems 245 -- 8.6 References for Further Study 247 -- 9 CLUSTER ANALYSIS 249 -- 9.1 Clustering Concepts 250 -- 9.2 Similarity Measures 253 -- 9.3 Agglomerative Hierarchical Clustering 259 -- 9.4 Partitional Clustering 263 -- 9.5 Incremental Clustering 266 -- 9.6 DBSCAN Algorithm 270 -- 9.7 BIRCH Algorithm 272 -- 9.8 Clustering Validation 275 -- 9.9 Review Questions and Problems 275 -- 9.10 References for Further Study 279 -- 10 ASSOCIATION RULES 280 -- 10.1 Market-Basket Analysis 281 -- 10.2 Algorithm Apriori 283 -- 10.3 From Frequent Itemsets to Association Rules 285 -- 10.4 Improving the Effi ciency of the Apriori Algorithm 286 -- 10.5 FP Growth Method 288 -- 10.6 Associative-Classifi cation Method 290 -- 10.7 Multidimensional Association-Rules Mining 293 -- 10.8 Review Questions and Problems 295 -- 10.9 References for Further Study 298 -- 11 WEB MINING AND TEXT MINING 300 -- 11.1 Web Mining 300 -- 11.2 Web Content, Structure, and Usage Mining 302 -- 11.3 HITS and LOGSOM Algorithms 305 -- 11.4 Mining Path-Traversal Patterns 310 -- 11.5 PageRank Algorithm 313 -- 11.6 Text Mining 316 -- 11.7 Latent Semantic Analysis (LSA) 320 -- 11.8 Review Questions and Problems 324 -- 11.9 References for Further Study 326.
505 8# - FORMATTED CONTENTS NOTE
Remark 2 12 ADVANCES IN DATA MINING 328 -- 12.1 Graph Mining 329 -- 12.2 Temporal Data Mining 343 -- 12.3 Spatial Data Mining (SDM) 357 -- 12.4 Distributed Data Mining (DDM) 360 -- 12.5 Correlation Does Not Imply Causality 369 -- 12.6 Privacy, Security, and Legal Aspects of Data Mining 376 -- 12.7 Review Questions and Problems 381 -- 12.8 References for Further Study 382 -- 13 GENETIC ALGORITHMS 385 -- 13.1 Fundamentals of GAs 386 -- 13.2 Optimization Using GAs 388 -- 13.3 A Simple Illustration of a GA 394 -- 13.4 Schemata 399 -- 13.5 TSP 402 -- 13.6 Machine Learning Using GAs 404 -- 13.7 GAs for Clustering 409 -- 13.8 Review Questions and Problems 411 -- 13.9 References for Further Study 413 -- 14 FUZZY SETS AND FUZZY LOGIC 414 -- 14.1 Fuzzy Sets 415 -- 14.2 Fuzzy-Set Operations 420 -- 14.3 Extension Principle and Fuzzy Relations 425 -- 14.4 Fuzzy Logic and Fuzzy Inference Systems 429 -- 14.5 Multifactorial Evaluation 433 -- 14.6 Extracting Fuzzy Models from Data 436 -- 14.7 Data Mining and Fuzzy Sets 441 -- 14.8 Review Questions and Problems 443 -- 14.9 References for Further Study 445 -- 15 VISUALIZATION METHODS 447 -- 15.1 Perception and Visualization 448 -- 15.2 Scientifi c Visualization and -- Information Visualization 449 -- 15.3 Parallel Coordinates 455 -- 15.4 Radial Visualization 458 -- 15.5 Visualization Using Self-Organizing Maps (SOMs) 460 -- 15.6 Visualization Systems for Data Mining 462 -- 15.7 Review Questions and Problems 467 -- 15.8 References for Further Study 468 -- Appendix A 470 -- A.1 Data-Mining Journals 470 -- A.2 Data-Mining Conferences 473 -- A.3 Data-Mining Forums/Blogs 477 -- A.4 Data Sets 478 -- A.5 Comercially and Publicly Available Tools 480 -- A.6 Web Site Links 489 -- Appendix B: Data-Mining Applications 496 -- B.1 Data Mining for Financial Data Analysis 496 -- B.2 Data Mining for the Telecomunications Industry 499 -- B.3 Data Mining for the Retail Industry 501 -- B.4 Data Mining in Health Care and Biomedical Research 503 -- B.5 Data Mining in Science and Engineering 506.
505 8# - FORMATTED CONTENTS NOTE
Remark 2 B.6 Pitfalls of Data Mining 509 -- Bibliography 510 -- Index 529.
520 ## - SUMMARY, ETC.
Summary, etc Now updated-the systematic introductory guide to modern analysis of large data setsAs data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making.This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies:. Support Vector Machines (SVM)-developed based on statistical learning theory, they have a large potential for applications in predictive data mining. Kohonen Maps (Self-Organizing Maps - SOM)-one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations. DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms-representatives of an important class of density-based clustering methodologies. Bayesian Networks (BN) methodology often used for causality modeling. Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks. CART algorithm and Gini index in building decision trees. Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm. Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning. PageRank algorithm for mining and authority ranking of web pages. Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents. New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here.This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here.
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6105606
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, New Jersey :
-- John Wiley,
-- c2011.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [2011]
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
588 ## -
-- Description based on PDF viewed 12/21/2015.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
695 ## -
-- Accuracy
695 ## -
-- Adaptation models
695 ## -
-- Algorithm design and analysis
695 ## -
-- Analytical models
695 ## -
-- Approximation algorithms
695 ## -
-- Artificial neural networks
695 ## -
-- Banking
695 ## -
-- Bibliographies
695 ## -
-- Biological cells
695 ## -
-- Biology
695 ## -
-- Brain modeling
695 ## -
-- Business
695 ## -
-- Chemicals
695 ## -
-- Clustering algorithms
695 ## -
-- Communications technology
695 ## -
-- Companies
695 ## -
-- Computational modeling
695 ## -
-- Computers
695 ## -
-- Data analysis
695 ## -
-- Data mining
695 ## -
-- Data models
695 ## -
-- Data visualization
695 ## -
-- Databases
695 ## -
-- Decision trees
695 ## -
-- Design automation
695 ## -
-- Dispersion
695 ## -
-- Distributed databases
695 ## -
-- Encoding
695 ## -
-- Error analysis
695 ## -
-- Estimation
695 ## -
-- Evolution (biology)
695 ## -
-- Fuzzy logic
695 ## -
-- Fuzzy sets
695 ## -
-- Generators
695 ## -
-- Genetic algorithms
695 ## -
-- Genetics
695 ## -
-- Hypercubes
695 ## -
-- Image color analysis
695 ## -
-- Indexes
695 ## -
-- Internet
695 ## -
-- Investments
695 ## -
-- Knowledge engineering
695 ## -
-- Learning systems
695 ## -
-- Machine learning
695 ## -
-- Mathematical model
695 ## -
-- Measurement units
695 ## -
-- Neurons
695 ## -
-- Noise measurement
695 ## -
-- Optimization
695 ## -
-- Partitioning algorithms
695 ## -
-- Prediction algorithms
695 ## -
-- Predictive models
695 ## -
-- Process control
695 ## -
-- Reliability
695 ## -
-- Risk management
695 ## -
-- Servers
695 ## -
-- Social network services
695 ## -
-- Statistical analysis
695 ## -
-- Supervised learning
695 ## -
-- Temperature measurement
695 ## -
-- Training
695 ## -
-- Training data
695 ## -
-- Vectors
695 ## -
-- Visualization
695 ## -
-- Web mining
695 ## -
-- Web pages

No items available.