Data Mining: Concepts, Models, Methods, and Algorithms, 2th Edition

Data Mining: Concepts, Models, Methods, and Algorithms, 2th Edition 2011 | ISBN: 0470890452 | 552 pages | EPUB | 9,2 MB Now updated-the s...





Data Mining: Concepts, Models, Methods, and Algorithms, 2th Edition
2011 | ISBN: 0470890452 | 552 pages | EPUB | 9,2 MB


Now updated-the systematic introductory self-help guide to modern analysis of huge data setsAs data sets expanding in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more technical 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 after which describes representative state-of-the-art methods and algorithms originating from different disciplines like statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are given with necessary explanations and illustrative examples, and questions and exercises for practice following each chapter. This new edition features these new techniques/methodologies:
Support Vector Machines (SVM)-developed according to statistical learning theory, there is a large risk of 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 by causality modeling
Algorithms for measuring Betweeness and Centrality parameters in graphs, essential for applications in mining large social networks
CART algorithm and Gini index in building decision trees
Bagging & Boosting ways to ensemble-learning methodologies, with information on 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 elements of data mining technology
This text offers assistance with how and when to use a particular software program (using the companion data sets) from one of the hundreds offered when faced with a data set to mine. This allows analysts to make and perform their own data mining experiments employing their knowledge of the methodologies and techniques provided. The book emphasizes selecting appropriate methodologies and data analysis software, and also parameter tuning. These essential, qualitative decisions is only able to be made while using deeper knowledge of parameter meaning and its role in the technique which is offered here.


This volume is primarily intended as being 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 all the appropriate background may also successfully comprehend all topics presented here.


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