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What is Fuzzy Clustering

Chapter 3
Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics
Clustering undertaken where there are grades of membership of between 0 to 1 objects to each cluster.
Published in Chapter:
Fuzzy Clustering: An Analysis of Service Quality in the Mobile Phone Industry
Mashhour H. Baeshen (Cardiff University, UK), Malcolm J. Beynon (Cardiff University, UK), and Kate L. Daunt (Cardiff University, UK)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/978-1-5225-0997-4.ch003
Abstract
This chapter presents a study of the development of the clustering methodology to data analysis, with particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem concerning service quality (using SERVQUAL) of mobile phone users, and subsequent loyalty and satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed on the full SERVQUAL dimensions, and the established results interpreted before tested on external variables, namely the level of loyalty and satisfaction across the different clusters established.
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More Results
Chapter 3
Introduction to Fuzzy Data Mining Methods
A family of methods that aims to partition the data set into clusters in a way that objects are allowed to belong to several clusters simultaneously with different degrees of membership.
Published in Chapter: Introduction to Fuzzy Data Mining Methods; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 18
Data Analytics in Industry 4.0: In the Perspective of Big Data
Decomposition of data elements with respect to each element could be belong to one or more classes via a set of membership functions.
Published in Chapter: Data Analytics in Industry 4.0: In the Perspective of Big Data; From: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
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Chapter 60
Data Clustering
Also known as Soft Clustering. For fuzzy clustering, an object can be classified with fractional membership into multiple groups, in contrast to Hard Clustering where an object can be classified into one group only.
Published in Chapter: Data Clustering; From: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
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Chapter 2
A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques
Decomposition of data elements with respect to each element could be belong to one or more classes via a set of membership functions.
Published in Chapter: A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques; From: Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics
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Chapter 119
Fuzzy Logic-Based Classification and Authentication of Beverages
It is done for a set of data points based on their similarity values.
Published in Chapter: Fuzzy Logic-Based Classification and Authentication of Beverages; From: Encyclopedia of Data Science and Machine Learning
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Chapter 7
Big Data Mining Based on Computational Intelligence and Fuzzy Clustering
It is more flexible then crisp methods. Fuzzy clustering allows objects belong to several clusters with different degree of membership. Each column of the fuzzy portioning sum must equal to 1.
Published in Chapter: Big Data Mining Based on Computational Intelligence and Fuzzy Clustering; From: Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence
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Chapter 8
Applying the Immunological Network Concept to Clustering Document Collections
Clustering which splits data into overlapping clusters, where each object can belong, in some degree, to more than one cluster. Thus each cluster is treated as a fuzzy subset of objects and the membership function defining this subset represents the degrees of membership of each item to the subset. Major algorithm based on fuzzy paradigm is Fuzzy K-Means (Bezdek & Pal, 1992)
Published in Chapter: Applying the Immunological Network Concept to Clustering Document Collections; From: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
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Chapter 21
Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance
It is a family of clustering methods that partition a set of given data objects into (nondisjoint) fuzzy clusters. Each fuzzy cluster is a fuzzy set, and each object’s membership degree in all the clusters sum up to one.
Published in Chapter: Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance; From: Handbook of Research on Fuzzy Information Processing in Databases
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