NitroFlare Data Mining Concepts, Models, Methods, and Algorithms (2nd edition)

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    kocogi Active Member

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    Data Mining: Concepts, Models, Methods, and Algorithms (2nd edition) By Mehmed Kantardzic
    2011 | 552 Pages | ISBN: 0470890452 | EPUB + PDF | 13 MB

    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 technology
    This 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.
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