Intelligent data mining and knowledge discovery

Author: Jiao Licheng, Liu Fang, Gou Shuiping, Liu Jing, Chen Li
Publisher:
Publish Date: 2006-08-01
Features: Faced with the challenge of "people being drowned in data yet hungry for knowledge," data mining and knowledge discovery technologies emerged and flourished. Data mining involves fields such as artificial intelligence, pattern recognition, machine learning, and statistics. Therefore, we reflect the interdisciplinary knowledge crossover and new achievements of contemporary scientific and technological development in the textbook. At the same time, this book focuses on introducing how new technologies in intelligent information processing are applied to the field of data mining, such as intelligent search, classification, clustering, and intelligent decision-making. Based on the introduction of the theory, methods, and techniques of intelligent information processing, this book comprehensively and systematically introduces the concepts, principles, and applications of data mining. The book is divided into 9 chapters. Chapter 1 introduces the basic concepts, research status, and development directions of data mining and knowledge discovery from a general perspective; Chapter 2 introduces the theoretical foundations of data mining; Chapter 3 elaborates in detail the theoretical foundations of computational intelligence methods used for data mining; Chapter 4 discusses the classification methods of neural networks and evolutionary computing; Chapter 5 comprehensively discusses support vector machines and kernel classification methods; Chapter 6 elaborates in detail the integrated classification methods; Chapter 7 systematically discusses large-scale data clustering methods in data mining; Chapter 8 discusses association rule mining methods; and Chapter 9 introduces data mining examples and visualization. From Chapter 3 onwards, each chapter provides the experimental setup and results of the methods used. This book can serve as a textbook for senior undergraduate or graduate students in computer science, signal and information processing, applied mathematics, and other related fields, as well as a reference for scientists and researchers engaged in data mining research.
Facing the challenge of "people being drowned in data yet hungry for knowledge," data mining and knowledge discovery technologies emerged and flourished. Data mining involves fields such as artificial intelligence, pattern recognition, machine learning, and statistics. Therefore, we reflect the interdisciplinary knowledge crossover and new achievements of contemporary scientific and technological development in the textbook. At the same time, this book focuses on introducing how new technologies in intelligent information processing are applied to the field of data mining, such as intelligent search, classification, clustering, and intelligent decision-making. Based on the introduction of the theory, methods, and techniques of intelligent information processing, this book comprehensively and systematically introduces the concepts, principles, and applications of data mining. The book is divided into 9 chapters. Chapter 1 introduces the basic concepts, research status, and development directions of data mining and knowledge discovery from a general perspective; Chapter 2 introduces the theoretical foundations of data mining; Chapter 3 elaborates in detail the theoretical foundations of computational intelligence methods used for data mining; Chapter 4 discusses the classification methods of neural networks and evolutionary computing; Chapter 5 comprehensively discusses support vector machines and kernel classification methods; Chapter 6 elaborates in detail the integrated classification methods; Chapter 7 systematically discusses large-scale data clustering methods in data mining; Chapter 8 discusses association rule mining methods; and Chapter 9 introduces data mining examples and visualization. From Chapter 3 onwards, each chapter provides the experimental setup and results of the methods used. This book can serve as a textbook for senior undergraduate or graduate students in computer science, signal and information processing, applied mathematics, and other related fields, as well as a reference for scientists and researchers engaged in data mining research.