Mehmed Kantardzic – författare

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. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.

 

If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com

If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org

 

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. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.

 

If you are an instructor or professor and would like to obtain instructor’s materials, please visit http://booksupport.wiley.com

If you are an instructor or professor and would like to obtain a solutions manual, please send an email to: pressbooks@ieee.org

 

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.

Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spacesThe 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 dataWritten 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.

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.

This book constitutes selected and revised papers from the First Mediterranean Forum - Data Science Conference, MeFDATA 2020, held in Sarajevo, Bosnia and Herzegovina, in October 2020. The 11 papers presented were carefully reviewed and selected from the 26 qualified submissions. The papers are organized in the topical sections on human behaviour and pandemic; applications in medicine; industrial applications; natural language processing.

This book constitutes selected and revised papers from the First Mediterranean Forum - Data Science Conference, MeFDATA 2020, held in Sarajevo, Bosnia and Herzegovina, in October 2020. The 11 papers presented were carefully reviewed and selected from the 26 qualified submissions. The papers are organized in the topical sections on human behaviour and pandemic; applications in medicine; industrial applications; natural language processing.

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.