Information Fusion In Data Mining
Download Information Fusion In Data Mining PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Information Fusion In Data Mining book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Fuzzy Systems and Data Mining V
The Fuzzy Systems and Data Mining (FSDM) conference is an annual event encompassing four main themes: fuzzy theory, algorithms and systems, which includes topics like stability, foundations and control; fuzzy application, which covers different kinds of processing as well as hardware and architectures for big data and time series and has wide applicability; the interdisciplinary field of fuzzy logic and data mining, encompassing applications in electrical, industrial, chemical and engineering fields as well as management and environmental issues; and data mining, outlining new approaches to big data, massive data, scalable, parallel and distributed algorithms. The annual conference provides a platform for knowledge exchange between international experts, researchers, academics and delegates from industry. This book includes the papers accepted and presented at the 5th International Conference on Fuzzy Systems and Data Mining (FSDM 2019), held in Kitakyushu, Japan on 18-21 October 2019. This year, FSDM received 442 submissions. All papers were carefully reviewed by program committee members, taking account of the quality, novelty, soundness, breadth and depth of the research topics falling within the scope of FSDM. The committee finally decided to accept 137 papers, which represents an acceptance rate of about 30%. The papers presented here are arranged in two sections: Fuzzy Sets and Data Mining, and Communications and Networks. Providing an overview of the most recent scientific and technological advances in the fields of fuzzy systems and data mining, the book will be of interest to all those working in these fields.
Fundamentals of Digital Manufacturing Science
Author: Zude Zhou
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-10-22
The manufacturing industry will reap significant benefits from encouraging the development of digital manufacturing science and technology. Digital Manufacturing Science uses theorems, illustrations and tables to introduce the definition, theory architecture, main content, and key technologies of digital manufacturing science. Readers will be able to develop an in-depth understanding of the emergence and the development, the theoretical background, and the techniques and methods of digital manufacturing science. Furthermore, they will also be able to use the basic theories and key technologies described in Digital Manufacturing Science to solve practical engineering problems in modern manufacturing processes. Digital Manufacturing Science is aimed at advanced undergraduate and postgraduate students, academic researchers and researchers in the manufacturing industry. It allows readers to integrate the theories and technologies described with their own research works, and to propose new ideas and new methods to improve the theory and application of digital manufacturing science.
Data Science in Practice
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.