Chinese Computer Society Academic Works Series: Knowledge Discovery

Author: Shi Zhongzhi
Publisher:
Publish Date: 2002-01-01
Features: Knowledge discovery is a non-trivial process of identifying effective, novel, potentially useful, and ultimately understandable patterns from data sets. Knowledge discovery transforms information into knowledge, finding knowledge nuggets from data mines, which will contribute to knowledge innovation and the development of the knowledge economy. This book comprehensively and systematically introduces methods and techniques of knowledge discovery, reflecting the latest achievements in current research on knowledge discovery. The book is divided into 14 chapters. Chapter 1 is an introduction, introducing important concepts and tasks of knowledge discovery. Chapter 2 discusses decision trees, which are one of the most practical techniques in inductive learning methods. Association rule mining is one of the most widely applied methods in recent years, and Chapter 3 will discuss important association rule mining algorithms. Chapter 4 discusses case-based reasoning, which is an effective and practical technique. Chapter 5 explores fuzzy clustering methods. Chapter 6 discusses rough sets. Chapter 7 is about Bayesian networks, which can handle incomplete and noisy data sets and use probabilistic measures to describe the correlation between data. Chapter 8 explores support vector machines, which have been an extremely active research topic in knowledge discovery research in recent years. Chapter 9 discusses hidden Markov models. Chapter 10 is about neural networks, with a focus on introducing several practical algorithms. Chapter 11 discusses evolutionary and genetic algorithms. Chapter 12 introduces the knowledge discovery platform MSMiner. Next, using web knowledge discovery and bioinformatics processing as examples, the application of knowledge discovery is introduced. Chapter 13 is about web knowledge discovery. Chapter 14 introduces the discovery of genomic patterns in bioinformatics processing. The content of this book is novel, carefully summarizing the author's research achievements, drawing on the latest domestic and international materials, and reflecting the current research level in this field. The discussion strives for clear concepts, accurate expression, and highlights the connection between theory and practice. Principles are illustrated through examples, making the book inspiring. This book is of great reference value for scientists and researchers engaged in knowledge discovery, data mining, machine learning, artificial intelligence research, and knowledge management. It can also be used as a textbook for Ph.D. and master's students in computer science and information technology.