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.

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.

Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov Model

2016 ◽  
Vol 7 (2) ◽  
pp. 23-44 ◽  
Author(s):  
Sharmila Subudhi ◽  
Suvasini Panigrahi ◽  
Tanmay Kumar Behera
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mobile Phone ◽  
Hidden Markov ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Novel Approach ◽  
Incoming Call ◽  
Fuzzy C Means Clustering ◽  
Reality Mining

This paper presents a novel approach for fraud detection in mobile phone networks by using a combination of Possibilistic Fuzzy C-Means clustering and Hidden Markov Model (HMM). The clustering technique is first applied on two calling features extracted from the past call records of a subscriber generating a behavioral profile for the user. The HMM parameters are computed from the profile, which are used to generate some profile sequences for training. The trained HMM model is then applied for detecting fraudulent activities on incoming call sequences. A calling instance is detected as forged when the new sequence is not accepted by the trained model with sufficiently high probability. The efficacy of the proposed system is demonstrated by extensive experiments carried out with Reality Mining dataset. Furthermore, the comparative analysis performed with other clustering methods and another approach recently proposed in the literature justifies the effectiveness of the proposed algorithm.

scholarly journals PENERAPAN METODE FUZZY C-MEANS PADA PENGELOMPOKAN PASIEN KANKER PAYUDARA PASCA OPERASI MENGGUNAKAN HABERMAN’S SURVIVAL DATASET

2018 ◽  
Vol 1 (2) ◽  
pp. 42-46
Author(s):  
Hetty Rohayani AH ◽  
Afrizal J
Keyword(s):  
Fuzzy Clustering ◽  
Data Set ◽  
Fuzzy C Means

Kanker payudara adalah penyakit yang merupakan urutan kedua sebagai penyebab kematian di dunia. Dimana penanganan kanker payudara diantaranya dilakukan dengan operasi, tetapi penanganan kanker payudara dengan jalan operasi tidaklah merupakan suatu jalan mengatasi masalah, karena kemudian masalah yang timbul adalah kebertahanan hidup pasien pasca dilakukannya operasi. Dalam penelitian ini dengan menggunakan metode fuzzy clustering means mencoba mengelompokan pasien kanker payudara pasca dilakukan operasi, dimana pengelompokan dijadikan dua kelompok yang pertama adalah kebertahanan hidup pasien kanker payudara pasca operasi lebih dari lima tahun dan kebertahanan hidup pasien pasca operasi kanker payudara yang kurang dari lima tahun. Dihasilkan pengelompokan berdasarkan atribut yang meliputi usia saat pasien terkena kanker, tahun dilakukannya operasi dan identitas secara medis. Yang mana berhasil mengelompokan data yang berasal dari data set haberman survival menjadi dua kelompok.

scholarly journals Transformer condition assessment using fuzzy C-means clustering techniques

2019 ◽  
Vol 35 (2) ◽  
pp. 47-55 ◽  
Author(s):  
Samuel Eke ◽  
Guy Clerc ◽  
Thomas Aka-Ngnui ◽  
I. Fofana
Keyword(s):  
Condition Assessment ◽  
Clustering Techniques ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering
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