A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis

2014 ◽  
Author(s):  
M. Prasad ◽  
L. Siana ◽  
D. L. Li ◽  
C. T. Lin ◽  
Y. T. Liu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering

Fuzzy Clustering

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Mashhour H. Baeshen ◽  
Malcolm J. Beynon ◽  
Kate L. Daunt
Keyword(s):  
Data Analysis ◽  
Service Quality ◽  
Mobile Phone ◽  
Fuzzy Clustering ◽  
Data Set ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy Environment ◽  
External Variables ◽  
Fuzzy C Means Clustering

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.

Fuzzy Clustering with Repulsive Prototypes

2010 ◽  
pp. 332-346
Author(s):  
Frank Rehm ◽  
Roland Winkler ◽  
Rudolf Kruse
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Experimental Results ◽  
Number Of Clusters ◽  
Fuzzy C Means ◽  
Analysis Process ◽  
Fuzzy C Means Clustering ◽  
The Right

A well known issue with prototype-based clustering is the user’s obligation to know the right number of clusters in a dataset in advance or to determine it as a part of the data analysis process. There are different approaches to cope with this non-trivial problem. This chapter follows the approach to address this problem as an integrated part of the clustering process. An extension to repulsive fuzzy c-means clustering is proposed equipping non-Euclidean prototypes with repulsive properties. Experimental results are presented that demonstrate the feasibility of the authors’ technique.

Fuzzified Even-Sized Clustering Based on Optimization

2018 ◽  
Vol 22 (4) ◽  
pp. 537-543 ◽  
Author(s):  
Kei Kitajima ◽  
Yasunori Endo ◽  
Yukihiro Hamasuna ◽  
◽  
◽  
...  
Keyword(s):  
Data Analysis ◽  
Fuzzy Clustering ◽  
Cluster Size ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Numerical Examples ◽  
Fuzzy Clustering Method

Clustering is a method of data analysis without the use of supervised data. Even-sized clustering based on optimization (ECBO) is a clustering algorithm that focuses on cluster size with the constraints that cluster sizes must be the same. However, this constraints makes ECBO inconvenient to apply in cases where a certain margin of cluster size is allowed. It is believed that this issue can be overcome by applying a fuzzy clustering method. Fuzzy clustering can represent the membership of data to clusters more flexible. In this paper, we propose a new even-sized clustering algorithm based on fuzzy clustering and verify its effectiveness through numerical examples.

Sign in / Sign up

Export Citation Format

Share Document