Library Services | Technical University of Kenya

Your cart is empty.
Normal view MARC view ISBD view

Distances and Similarities in Intuitionistic Fuzzy Sets [electronic resource] / by Eulalia Szmidt.

By: Szmidt, Eulalia [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Fuzziness and Soft Computing: 307Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: VIII, 148 p. 35 illus., 17 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319016405.Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Operations Research, Management Science | Artificial Intelligence (incl. Robotics)DDC classification: 006.3 Online resources: Click here to access online
Contents:
Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets -- Distances -- Similarity Measures between Intuitionistic Fuzzy Sets.
In: Springer eBooksSummary: This book presents the state-of-the-art in theory and practice regarding similarity and distance measures for intuitionistic fuzzy sets. Quantifying similarity and distances is crucial for many applications, e.g. data mining, machine learning, decision making, and control. The work provides readers with a comprehensive set of theoretical concepts and practical tools for both defining and determining similarity between intuitionistic fuzzy sets. It describes an automatic algorithm for deriving intuitionistic fuzzy sets from data, which can aid in the analysis of information in large databases. The book also discusses other important applications, e.g. the use of similarity measures to evaluate the extent of agreement between experts in the context of decision making.
Tags from this library: No tags from this library for this title. Log in to add tags.
No physical items for this record

Intuitionistic Fuzzy Sets as a Generalization of Fuzzy Sets -- Distances -- Similarity Measures between Intuitionistic Fuzzy Sets.

This book presents the state-of-the-art in theory and practice regarding similarity and distance measures for intuitionistic fuzzy sets. Quantifying similarity and distances is crucial for many applications, e.g. data mining, machine learning, decision making, and control. The work provides readers with a comprehensive set of theoretical concepts and practical tools for both defining and determining similarity between intuitionistic fuzzy sets. It describes an automatic algorithm for deriving intuitionistic fuzzy sets from data, which can aid in the analysis of information in large databases. The book also discusses other important applications, e.g. the use of similarity measures to evaluate the extent of agreement between experts in the context of decision making.

There are no comments for this item.

Log in to your account to post a comment.

2017 | The Technical University of Kenya Library | +254(020) 2219929, 3341639, 3343672 | library@tukenya.ac.ke | Haile Selassie Avenue