Machine Learning and Data Mining: Methods and Applications

Author: Mihalski
Publisher:
Publish Date: 2004-01-01
Features: With the increasing application of database technology, people are gradually falling into the awkward situation of "abundant data but scarce knowledge." Against this backdrop, data mining technology, also known as database knowledge discovery, began to rapidly rise in the 1990s. This field of information is based on computer technology from multiple disciplines such as machine learning and statistical analysis. It can effectively help people transform vast data resources into useful knowledge and information resources, thereby enabling them to make scientific decisions. Machine learning is a discipline that studies computational methods in the learning process and how to apply computer-based learning systems to solve practical problems. An important research area in machine learning is the study of methods for obtaining corresponding conceptual descriptions from samples. Therefore, many machine learning methods can be directly applied to solve data mining problems. Data mining, in essence, is the problem of searching for interesting patterns and important rules from large-scale databases. This book is primarily written for non-professionals in machine learning or data mining who are interested in the applications and introductory knowledge of these fields. Therefore, the book collects practical cases of data mining from various fields to illustrate specific solutions and aims to provide readers with a broader perspective on data mining applications. This is the major difference between this book and other data mining books. The book is divided into five parts and 18 chapters, providing a comprehensive introduction to the basic concepts of machine learning and discussing related issues in data mining and knowledge discovery, as well as multi-strategy learning methods. It specifically elaborates on the applications of machine learning and data mining in various fields, including engineering design, text, images, and music; web analysis, computer viruses, and computer control; medical diagnosis, biomedical signal analysis, and bio-signal processing for water quality analysis. The book collects numerous practical cases of data mining from different fields to illustrate specific solutions and aims to provide readers with a broader perspective on data mining applications. The readers of this book can be any engineering technicians, business managers, or other personnel interested in machine learning and data mining. It can also serve as an important supplementary teaching material for relevant courses in colleges and universities.