Data mining : concepts, models, methods, and algorithms / Mehmed Kantardzic
By: Kantardzic, Mehmed.
Contributor(s): Ohio Library and Information Network.
Publisher: [Piscataway, NJ] : Hoboken, New Jersey : IEEE Press ; Wiley, ©2020Copyright date: ©2020Edition: Third edition.Description: 639 p.Content type: text Media type: computer Carrier type: online resourceISBN: 9781119516040; 1119516048.Subject(s): Data miningGenre/Form: Print books.| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | QA76.9.D343 K35 2020 (Browse shelf) | Available | AU00000000016829 |
Includes bibliographical references and index
T Data-Mining Concepts -- Preparing the Data -- Data Reduction -- Learning from Data -- Statistical Methods -- Decision Trees and Decision Rules -- Artificial Neural Networks -- Ensemble Learning -- Cluster Analysis -- Association Rules -- Web Mining and Text Mining -- Advances in Data Mining -- Genetic Algorithms -- Fuzzy sets and Fuzzy Logic -- Visualization Methods -- Appendix A -- Appendix B: Data-Mining Applications
Available to OhioLINK libraries
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author'a noted expert on the topic'explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter. This third edition presents new and expanded information that: -''' Explores big data and cloud computing -''' Examines deep learning -''' Includes information on convolutional neural networks (CNN) -''' Offers reinforcement learning -''' Contains semi-supervised learning and S3VM -''' Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field