國立虎尾科技大學 |

Fuzziness in Information Systems = How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Fuzziness in Information Systems/ by Miroslav Hudec.
其他題名:	How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /
作者:	Hudec, Miroslav.
面頁冊數:	XXII, 198 p. 91 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Data mining. -
電子資源:	https://doi.org/10.1007/978-3-319-42518-4
ISBN:	9783319425184

Fuzziness in Information Systems = How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /
Hudec, Miroslav.

Fuzziness in Information SystemsHow to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /[electronic resource] :by Miroslav Hudec. - 1st ed. 2016. - XXII, 198 p. 91 illus.online resource.

1 Fuzzy Set and Fuzzy Logic Theory in Brief -- 2 Fuzzy Queries -- 3 Linguistic Summaries -- 4 Fuzzy Inference -- 5 Fuzzy Data in Relational Databases -- 6 Perspectives, Synergies and Conclusion -- A Illustrative Interfaces and Applications for Fuzzy Queries -- B Illustrative Interfaces and Applications for Linguistic Summaries. .

This book is an essential contribution to the description of fuzziness in information systems. Usually users want to retrieve data or summarized information from a database and are interested in classifying it or building rule-based systems on it. But they are often not aware of the nature of this data and/or are unable to determine clear search criteria. The book examines theoretical and practical approaches to fuzziness in information systems based on statistical data related to territorial units. Chapter 1 discusses the theory of fuzzy sets and fuzzy logic to enable readers to understand the information presented in the book. Chapter 2 is devoted to flexible queries and includes issues like constructing fuzzy sets for query conditions, and aggregation operators for commutative and non-commutative conditions, while Chapter 3 focuses on linguistic summaries. Chapter 4 presents fuzzy logic control architecture adjusted specifically for the aims of business and governmental agencies, and shows fuzzy rules and procedures for solving inference tasks. Chapter 5 covers the fuzzification of classical relational databases with an emphasis on storing fuzzy data in classical relational databases in such a way that existing data and normal forms are not affected. This book also examines practical aspects of user-friendly interfaces for storing, updating, querying and summarizing. Lastly, Chapter 6 briefly discusses possible integration of fuzzy queries, summarization and inference related to crisp and fuzzy databases. The main target audience of the book is researchers and students working in the fields of data analysis, database design and business intelligence. As it does not go too deeply into the foundation and mathematical theory of fuzzy logic and relational algebra, it is also of interest to advanced professionals developing tailored applications based on fuzzy sets.

ISBN: 9783319425184

Standard No.: 10.1007/978-3-319-42518-4doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Fuzziness in Information Systems = How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization /
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