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.

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.

scholarly journals A Weight Possibilistic Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiashun Chen ◽  
Hao Zhang ◽  
Dechang Pi ◽  
Mehmed Kantardzic ◽  
Qi Yin ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stock Market ◽  
Objective Function ◽  
Clustering Algorithm ◽  
Lagrange Equation ◽  
Experimental Results ◽  
Retail Market ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock market prediction. Especially, parameters in FCM have influence on clustering results. However, a lot of FCM algorithm did not solve the problem, that is, how to set parameters. In this study, we present a kind of method for computing parameters values according to role of parameters in the clustering process. New parameters are assigned to membership and typicality so as to modify objective function, on the basis of which Lagrange equation is constructed and iterative equation of membership is acquired, so does the typicality and center equation. At last, a new possibilistic fuzzy C-means based on the weight parameter algorithm (WPFCM) was proposed. In order to test the efficiency of the algorithm, some experiments on different datasets are conducted to compare WPFCM with FCM, possibilistic C-means (PCM), and possibilistic fuzzy C-means (PFCM). Experimental results show that iterative times of WPFCM are less than FCM about 25% and PFCM about 65% on dataset X12. Resubstitution errors of WPFCM are less than FCM about 19% and PCM about 74% and PFCM about 10% on the IRIS dataset.

scholarly journals MODEL PEMETAAN EVALUASI PENILAIAN KUALIFIKASI LULUSAN BERBASIS METODE FUZZY C_MEANS CLUSTERING

2014 ◽  
Vol 7 (2) ◽  
Author(s):  
Anif Hanifa Setianingrum
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Ag Cluster ◽  
Target Output

Dunia pendidikan sering mengalami masalah dengan tidak tercapainya tujuan yang telah ditetapkan dalam visi misi institusi. Banyak faktor yang menyebabkan tidak berjalan atau tidak tercapainya target output yang dihasilkan. Faktor-faktor internal SDM, metode pengajaran, serta kurikulum yang telah dirumuskan kadang tidak dapat memenuhi standarisasi kualifikasi dari pihak stakeholder. Metode evaluasi dan monitoring akan melakukan pemetaan permasalahan metode pengajaran dari para pelaksana institusi. Evaluasi Pemetaan dan Penerapan metode pengajaran dengan menggunakan Metode Fuzzy C-Means Clustering (FCM), dengan mengumpulkan data hasil penilaian dosen terhadap daftar nilai mahasiswa.. Penilaian juga harus dilakukan dengan hasil penilaian stakeholder.Hasil Cluster menyatakan ada Lima (5) cluster pengelompokkan Kualifikasi Mahasiswa (SO1, SO2, SO3) dan Identifikasi Penilaian SKKNI terhadap JRP  Cluster Pertama untuk K,V,AD,AG, Cluster Kedua  : D,H,O,W,AN, Cluster Ketiga untuk Mahasiswa A,M,R,T,AA,AJ, Cluster 4 Y,AC,AI,AK,AO, Cluster 5 E,I,J,N,AL.Ada persamaan dan ketidaksamaan nama mahasiswa dari hasil penilaian internal maupun hasil penilaian eksternal artinya Penilaian internal terhadap kualifikasi kelulusan mahasiswa berbeda dengan kriteria penilaian stakeholder terhadap standarisasi SKKNI.Kata Kunci: Fuzzy, Clustering, Standarisasi SKKNI, FCM

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

scholarly journals A Validity Index for Fuzzy Clustering Based on Bipartite Modularity

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yongli Liu ◽  
Xiaoyang Zhang ◽  
Jingli Chen ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Optimal Number ◽  
Experimental Results ◽  
Validity Index ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Noise Data ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we propose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite modularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically determine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on six real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs the best in terms of robustness while accurately detecting the number of clusters.

An Outlier Detection Method Based on Fuzzy C-Means Clustering

2009 ◽  
Vol 419-420 ◽  
pp. 165-168
Author(s):  
Qiang Li ◽  
Jian Pei Zhang ◽  
Guang Sheng Feng
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Data Mining Techniques ◽  
Fuzzy C Means ◽  
Second Stage ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Two Stages

Both fuzzy c-means (FCM) clustering and outlier detection are useful data mining techniques in real applications. In this paper, we show that the task of outlier detection could be achieved as by-product of fuzzy c-means clustering. The proposed strategy consists of two stages. The first stage consists of purely fuzzy c-means process, while the second stage identifies exceptional objects according to a novel metric based on the entropy of membership values. We provide experimental results to demonstrate the effectiveness of our technique.

scholarly journals Cluster Validity Index to Determine the Optimal Number Clusters of Fuzzy Clustering for Classify Customer Buying Behavior

2021 ◽  
Vol 5 (1) ◽  
pp. 7-12
Author(s):  
Salnan Ratih Asrriningtias
Keyword(s):  
Fuzzy Clustering ◽  
Optimal Number ◽  
Buying Behavior ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Number Of Clusters ◽  
Best Value ◽  
Fuzzy Clustering Method ◽  
The Right

One of the strategies in order to compete in Batik  MSMEs  is to look at the characteristics of the customer. To make it easier to see the characteristics of  customer buying behavior, it is necessary to classify customers based on similarity of characteristics using fuzzy clustering. One of the parameters that must be determined at the beginning of the fuzzy clustering method is the number of clusters. Increasing the number of clusters does not guarantee the best performance, but the right number of clusters greatly affects the performance of fuzzy clustering. So to get optimal number cluster, we can measured the result of clustering in each number cluster using the cluster validity index. From several types of cluster validity index,  NPC give the best value. Optimal number cluster that obtained by the validity index is 2 and this number cluster give classify result with small variance value

Penentuan Kelompok Jaringan Logistik pada Wilayah Kepulauan menggunakan Fuzzy C-Means

2018 ◽  
Vol 2 (2) ◽  
pp. 76
Author(s):  
Sinta Tri Kismanti ◽  
Andi Ard Maidah
Keyword(s):  
Distribution System ◽  
Movement Pattern ◽  
Number Of Clusters ◽  
Logistics Distribution ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Network Patterns

Indonesia as the island nation with territorial waters is one of the modes of transportation. For this condition, most of activities are conducted in marine, such as logistics distribution. The movement of logistics distribution will result in a movement pattern of the logistic. Determination of the optimal pattern of logistics movement network can support the smooth distribution system. Determination of logistic network patterns is done by clustering using Fuzzy C-means, clustering aims to get island groups in adjacent locations. The clustering process using Fuzzy C-Means obtained that the number of clusters as many as 3 clusters showed better results compared to the number of clusters 4 and 5.

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