The interdisciplinary field of fuzzy logic encompass applications in the electrical, industrial, chemical and engineering realms as well as in areas of management and environmental issues, while data mining covers new approaches to big data, massive data, and scalable, parallel and distributed algorithms. This book presents papers from the 6th International Conference on Fuzzy Systems and Data Mining (FSDM 2020). The conference was originally due to be held from 13-16 November 2020 in Xiamen, China, but was changed to an online conference held on the same dates due to ongoing restrictions connected with the COVID-19 pandemic. The annual FSDM conference provides a platform for knowledge exchange between international experts, researchers academics and delegates from industry. This year, the committee received 316 submissions, of which 76 papers were selected for inclusion in the conference; an acceptance rate of 24%. The conference covers four main areas: fuzzy theory; algorithms and systems, which includes topics like stability; foundations and control; and fuzzy applications, which are widely used and cover various types of processing as well as hardware and architecture for big data and time series. Providing a current overview of research and developments in fuzzy logic and data mining, the book will be of interest to all those working in the field of data science.

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision sup

In today's world of competitive business environment, there is a driving need to extract hidden and potentially meaningful information from large databases for effective decision making. This compact book explores the concept of data mining and discusses various data mining techniques and their applications. It is primarily designed for the students of Computer Science and Engineering, Information Technology, Computer Applications, and Management. Written in a student-friendly style, the book describes the various phases of data mining, architecture of a data mining system, and the types of knowledge that can be mined from databases. It elaborates on different data preprocessing techniques such as cleaning, integration, transformation and reduction. The text then explains the various data mining techniques such as association rule mining, data classification and clustering. The book adopts an algorithm-centric approach presenting various algorithms for these data mining techniques. Finally, the text ends with an exhaustive discussion on multimedia data mining (MDM). It illustrates the concepts with the help of various figures and examples. It provides a summary at the end of each chapter for quick revision of key points. It offers chapter-end questions for self-evaluation.

This book constitutes the refereed proceedings of the First International Conference on Advanced Data Mining and Applications, ADMA 2005, held in Wuhan, China in July 2005. The conference was focused on sophisticated techniques and tools that can handle new fields of data mining, e.g. spatial data mining, biomedical data mining, and mining on high-speed and time-variant data streams; an expansion of data mining to new applications is also strived for. The 25 revised full papers and 75 revised short papers presented were carefully peer-reviewed and selected from over 600 submissions. The papers are organized in topical sections on association rules, classification, clustering, novel algorithms, text mining, multimedia mining, sequential data mining and time series mining, web mining, biomedical mining, advanced applications, security and privacy issues, spatial data mining, and streaming data mining.

Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow KEY FEATURES ● Explore concepts ranging from fundamentals to advanced techniques of data warehouses and data mining. ● Translate business questions into actionable strategies to make informed decisions. ● Gain practical implementation guidance for hands-on learning. DESCRIPTION Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets. The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions. WHAT YOU WILL LEARN ● Designing and building efficient data warehouses. ● Handling diverse data types for comprehensive insights. ● Mastering various data mining techniques. ● Translating business questions into mining strategies. ● Techniques for pattern discovery and knowledge extraction. WHO THIS BOOK IS FOR From aspiring data analysts, data professionals, IT managers, to business intelligence practitioners, this book caters to a diverse audience. TABLE OF CONTENTS 1. Introduction to Data Warehousing 2. Data Warehouse Process and Architecture 3. Data Warehouse Implementation 4. Data Mining Definition and Task 5. Data Mining Query Languages 6. Data Mining Techniques 7. Mining Complex Data Objects

Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

This book offers a systematic overview of the concepts and practical techniques that readers need to get the most out of their large-scale data mining projects and research studies. It guides them through the data-analytical thinking essential to extract useful information and obtain commercial value from the data. Presenting the outcomes of International Conference on Soft Computing and Data Mining (SCDM-2017), held in Johor, Malaysia on February 6–8, 2018, it provides a well-balanced integration of soft computing and data mining techniques. The two constituents are brought together in various combinations of applications and practices. To thrive in these data-driven ecosystems, researchers, engineers, data analysts, practitioners, and managers must understand the design choice and options of soft computing and data mining techniques, and as such this book is a valuable resource, helping readers solve complex benchmark problems and better appreciate the concepts, tools, and techniques employed.

As cameras become more pervasive in our daily life, vast amounts of video data are generated. The popularity of YouTube and similar websites such as Tudou and Youku provides strong evidence for the increasing role of video in society. One of the main challenges confronting us in the era of information technology is to - fectively rely on the huge and rapidly growing video data accumulating in large multimedia archives. Innovative video processing and analysis techniques will play an increasingly important role in resolving the difficult task of video search and retrieval. A wide range of video-based applications have benefited from - vances in video search and mining including multimedia information mana- ment, human-computer interaction, security and surveillance, copyright prot- tion, and personal entertainment, to name a few. This book provides an overview of emerging new approaches to video search and mining based on promising methods being developed in the computer vision and image analysis community. Video search and mining is a rapidly evolving discipline whose aim is to capture interesting patterns in video data. It has become one of the core areas in the data mining research community. In comparison to other types of data mining (e. g. text), video mining is still in its infancy. Many challenging research problems are facing video mining researchers.