Histogram-Based Fuzzy Clustering and Its Comparison to Fuzzy C-Means Clustering in One-Dimensional Data

2002 ◽  
pp. 253-267
Author(s):  
A. Chong ◽  
T. D. Gedeon ◽  
K. W. Wong
Keyword(s):  
Fuzzy Clustering ◽  
One Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

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

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 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.

FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES

2000 ◽  
Vol 08 (06) ◽  
pp. 735-746 ◽  
Author(s):  
ANNETTE KELLER ◽  
FRANK KLAWONN
Keyword(s):  
Objective Function ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Or Groups ◽  
Fuzzy C Means Clustering ◽  
Influence Parameter ◽  
Individual Variables ◽  
Single Data

We introduce an objective function-based fuzzy clustering technique that assigns one influence parameter to each single data variable for each cluster. Our method is not only suited to detect structures or groups of data that are not uniformly distributed over the structure's single domains, but gives also information about the influence of individual variables on the detected groups. In addition, our approach can be seen as a generalization of the well-known fuzzy c-means clustering algorithm.

scholarly journals Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Yao Yang ◽  
Chengmao Wu ◽  
Yawen Li ◽  
Shaoyu Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Iterative Solution ◽  
Segmentation Algorithm ◽  
Gaussian Kernel ◽  
Fuzzy C Means ◽  
Different Types ◽  
Fuzzy C Means Clustering

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C-means clustering segmentation algorithm combining utilizing neighborhood spatial gray information with fuzzy membership information is proposed in this paper. The mean intensity information of neighborhood window is embedded into the objective function of the existing semisupervised fuzzy C-means clustering, and the Lagrange multiplier method is used to obtain its iterative expression corresponding to the iterative solution of the optimization problem. Meanwhile, the local Gaussian kernel function is used to map the pixel samples from the Euclidean space to the high-dimensional feature space so that the cluster adaptability to different types of image segmentation is enhanced. Experiment results performed on different types of noisy images indicate that the proposed segmentation algorithm can achieve better segmentation performance than the existing typical robust fuzzy clustering algorithms and significantly enhance the antinoise performance.

A Combinatorial Classifier for Error-Data in Joining Processes with Diverse-Granular Computing

2012 ◽  
Vol 548 ◽  
pp. 740-743
Author(s):  
Yi Lan Chen ◽  
Huan Bao Wang
Keyword(s):  
Fuzzy Clustering ◽  
Granular Computing ◽  
Classification Performance ◽  
Classification Model ◽  
Fuzzy Partition ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Hybrid Classification ◽  
Scale Granularity ◽  
Better Than

In this paper, we present a novel hybrid classification model with fuzzy clustering and design a newly combinatorial classifier for error-data in joining processes with diverse-granular computing, which is an ensemble of a naïve Bayes classifier with fuzzy c-means clustering. And we apply it to improve classification performance of traditional hard classifiers in more complex real-world situations. The fuzzy c-means clustering is applied to a fuzzy partition based on a given propositional function to augment the combinatorial classifier. This strategy would work better than a conventional hard classifier without fuzzy clustering. Proper scale granularity of objects contributes to higher classification performance of the combinatorial classifier. Our experimental results show the newly combinatorial classifier has improved the accuracy and stability of classification.

Comparison between Fuzzy C-means clustering and Fuzzy Clustering Subtractive in urban air pollution

2010 ◽  
Author(s):  
J.I Collazo-Cuevas ◽  
M. A. Aceves-Fernandez ◽  
E. Gorrostieta-Hurtado ◽  
J.C. Pedraza-Ortega ◽  
A. Sotomayor-Olmedo ◽  
...  
Keyword(s):  
Air Pollution ◽  
Fuzzy Clustering ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

FUZZY C-MEANS CLUSTERING ALGORITHM WITH TWO LAYERS AND ITS APPLICATION TO IMAGE SEGMENTATION BASED ON TWO-DIMENSIONAL HISTOGRAM

1994 ◽  
Vol 02 (03) ◽  
pp. 343-350 ◽  
Author(s):  
WEIXIN XIE ◽  
JIANZHUANG LIU
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Two Dimensional ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Hard Clustering

This paper presents a fast fuzzy c-means (FCM) clustering algorithm with two layers, which is a mergence of hard clustering and fuzzy clustering. The result of hard clustering is used to initialize the c cluster centers in fuzzy clustering, and then the number of iteration steps is reduced. The application of the proposed algorithm to image segmentation based on the two dimensional histogram is provided to show its computational efficience.

scholarly journals Sistem Deteksi kualitas Beras Berdasarkan Warna menggunakan Fuzzy C-Means Clustering Guna Membantu Tingkat Pengetahuan Masyarakat

2019 ◽  
Vol 3 (2) ◽  
pp. 32-38
Author(s):  
Dewi Lestari ◽  
Nurul Fadillah ◽  
Ahmad Ihsan
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Beras  merupakan bahan makanan pokok bangsa Indonesia. Tidak hanya di Indonesia, sebagian besar penduduk dunia juga memilih beras sebagai bahan makanan pokok utama. Semakin tingginya  konsumsi beras di Indonesia dapat memicu terjadinya perkembangan beras bebas produk, maka dari itu masyarakat yang cerdas harus lebih teliti dalam melihat warna beras, apakah warna beras tersebut bagus dan layak untuk di masak atau warna beras tersebut termasuk kategori warna beras tidak bagus.  Salah satu metode yang dapat digunakan untuk menyelesaikan permasalahan ini adalah metode Fuzzy C-Means. Algoritma Fuzzy C-Means merupakan satu algoritma yang mudah dan sering di gunakan dalam pengelompokkan data karena membuat suatu perkiraan yang efisien dan tidak memerlukan banyak parameter. Pada kasus penelitian ini akan menganalisis penerapan metode Fuzzy C-Means untuk mengelompokkan beras bagus dan beras tidak bagus berdasarkan warna beras, dengan menggunakan dua gambar objek yang di jadikan sebagai sampel data. Salah satu teknik fuzzy clustering adalah Fuzzy C-Means Clustering (FCM). FCM merupakan suatu teknik pengklasteran data yang keberadaan setap datanya dalam suatu cluster di tentukan oleh nilai/derajat keanggotaan tertentu. Beberapa penelitian telah menghasilkan kesimpulan bahwa metode Fuzzy C-Means dapat di gunakan untuk mengelompokkan data berdasarkan atribut-atribut tertentu.  Penerapan algorita Fuzzy C-Means dalam penentuan kategori warna beras di kelompokkan menjadi 2 cluster yaitu beras tidak bagus dan beras bagus. Dari sampel data yang diambil di peroleh 2 cluster berdasarkan kriteria mana yang lebih di kategorikan dengan nilai terbesar pada jarak akhir merupakan cluster warna beras yang bagus, sedangkan cluster dengan nilai terkecil merupakan cluster yang di kategorikan beras tidak bagus. Pada gambar objek ke-1 range nilai 0.1667 - 0.9877 untuk kategori beras bagus dan 0.2 - 0.1667 untuk kategori beras tidak bagus. Sementara pada gambar objek ke-2 yaitu dengan range 0.9583 - 0.9936 untuk kategori beras bagus dan 0.6742 - 0.9596 untuk kategori beras tidak bagus.

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