scholarly journals KLASTERISASI DATA PERTANIAN DI KABUPATEN LAMONGAN MENGGUNAKAN ALGORITMA K-MEANS DAN FUZZY C MEANS

2020 ◽  
Vol 5 (2) ◽  
pp. 86
Author(s):  
Arif Rohmatullah ◽  
Dinita Rahmalia ◽  
Mohammad Syaiful Pradana
Keyword(s):  
Euclidean Distance ◽  
Fuzzy C Means ◽  
Degree Of Membership

Di Indonesia, terdapat beberapa pekerja sebagai petani sebagai matapencaharian karena kebutuhan pokok pada pangan dan memiliki lahan pertanian yang luas. Karena terdapat perbedaan  luas lahan pertanian dan hasil produksi pertanian, maka diperlukan klasterisasi pada data pertanian. Tujuan klastering adalah untuk mengidentifikasi suatu kelompok data dari populasi data untuk menghasilkan sifat-sifat dari data itu sendiri. Pada penelitian ini akan digunakan dua metode yaitu : algoritma K-Means dan algoritma Fuzzy C Means (FCM). Algoritma K-Means dan algoritma FCM dapat mengklaster beberapa kecamatan di kabupaten Lamongan berdasarkan luas lahan pertanian dan hasil produksi pertanian. Pada algoritma K-Means, titik pusat klaster diupdate sehingga menghasilkan jumlahan euclidean distance yang minimum. Pada algoritma FCM, derajat keanggotaan (the degree of membership) diupdate sehingga menghasilkan nilai fungsi objective yang minimum. Berdasarkan hasil simulasi, kedua metode tersebut dapat mengklaster beberapa kecamatan di kabupaten Lamongan berdasarkan luas lahan pertanian dan hasil produksi pertanian.

scholarly journals Application Kohonen Network and Fuzzy C Means for Clustering Airports Based on Frequency of Flight

2018 ◽  
pp. 229-236 ◽  
Author(s):  
Dinita Rahmalia ◽  
Teguh Herlambang
Keyword(s):  
Neural Network ◽  
Objective Function ◽  
Euclidean Distance ◽  
Kohonen Network ◽  
Fuzzy C Means ◽  
Degree Of Membership ◽  
Demand Rate

In Indonesia, the demands of air tranportation for reaching destination increase rapidly. Based on the flight schedule in airports spreading in Indonesia, the airports have different flight demand rate so that it requires clustering. This research will use two methods for clustering : kohonen network and Fuzzy C Means (FCM).Kohonen network is the type neural network which uses unsupervised training.Kohonen network uses weight vectors for training while FCM uses degree of membership. Both kohonen network and FCM, inputs are represented by the number of departure and arrival of airline in one day. For kohonen network, we update weight matrices so that minimizing the sum of optimum euclidean distance. For FCM, we update degrees of membership so that minimizing the objective function value.From the simulations, we can cluster the airports based on the number of departure and arrival of airline.

scholarly journals A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data

2021 ◽  
Vol 15 ◽  
pp. 1759-1765
Author(s):  
Souad Azzouzi ◽  
Amal Hjouji ◽  
Jaouad EL- Mekkaoui ◽  
Ahmed EL Khalfi
Keyword(s):  
Euclidean Distance ◽  
Dimensional Space ◽  
Noisy Data ◽  
High Accuracy ◽  
Cluster Center ◽  
Fuzzy C Means ◽  
Clustering And Classification ◽  
The Comparative Study ◽  
Fuzzy C Means Algorithm ◽  
Statistical Clustering

The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibilistic Fuzzy C-Means (ImPFCM) by combining the PFCM algorithm with a very powerful statistical method. The properties of this new ImPFCM algorithm show that it is not only applicable on clusters of spherical shapes, but also on clusters of different sizes and densities. The results of the comparative study with very recent algorithms indicate the performance and the superiority of the proposed approach to easily group the datasets in a large-dimensional space and to use not only the Euclidean distance but more sophisticated standards norms, capable to deal with much more complicated problems. On the other hand, we have demonstrated that the ImPFCM algorithm is also capable of detecting the cluster center with high accuracy and performing satisfactorily in multiple environments with noisy data and outliers.

scholarly journals Gaussian Kernel Fuzzy C-Means Algorithm for Service Resource Allocation

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Wei Jiang ◽  
Xi Fang ◽  
Jianmei Ding
Keyword(s):  
Euclidean Distance ◽  
Service Management ◽  
Cluster Algorithm ◽  
Gaussian Kernel ◽  
Cluster Problem ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Service Resource ◽  
Data Points ◽  
Evaluation Information

With respect to the cluster problem of the evaluation information of mass customers in service management, a cluster algorithm of new Gaussian kernel FCM (fuzzy C-means) is proposed based on the idea of FCM. First, the paper defines a Euclidean distance formula between two data points and makes them cluster adaptively based on the distance classification approach and nearest neighbors in deleting relative data. Second, the defects of the FCM algorithm are analyzed, and a solution algorithm is designed based on the dual goals of obtaining a short distance between whole classes and long distances between different classes. Finally, an example is given to illustrate the results compared with the existing FCM algorithm.

scholarly journals Fuzzy Clustering Algorithms for Image Segmentation

2011 ◽  
Author(s):  
Alberto Rey ◽  
Alfonso Castro ◽  
Bernardino Arcay
Keyword(s):  
Fuzzy Clustering ◽  
Euclidean Distance ◽  
Spatial Information ◽  
Multicore Processors ◽  
Clustering Algorithms ◽  
Source Code ◽  
Spatial Constraints ◽  
3D Images ◽  
Fuzzy C Means ◽  
The Membership Function

In this document we present the implementation of three fuzzy clustering algorithms using the Insight Toolkit ITK. Firstly, we developed the conventional Fuzzy C-Means that will serve as the basis for the rest of the proposed algorithms. The next algorithms are the FCM with spatial constraints based on kernel-induced distance and the Modified Spatial Kernelized Fuzzy C-Means. Both of these introduce a Kernel function, replacing the Euclidean distance of the FCM, and spatial information into the membership function.These algorithms have been implemented in a threaded version to take advantage of the multicore processors. Moreover, providing an useful implementation make it possible that classes work with 2D/3D images, different kernels and spatial shapes.We included the source code as well as different 2D/3D examples, using several input parameters for the algorithms and obtaining the results generated on 2D/3D CT lung studies.

Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation

2019 ◽  
Vol 8 (4) ◽  
pp. 25-38
Author(s):  
Srujan Sai Chinta
Keyword(s):  
Image Segmentation ◽  
Euclidean Distance ◽  
Firefly Algorithm ◽  
Clustering Algorithms ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Radial Basis

Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.

A fuzzy weighted c-means classification method for traffic flow state division

2021 ◽  
pp. 2150341
Author(s):  
Liangliang Zhang ◽  
Yuanhua Jia ◽  
Dongye Sun ◽  
Yang Yang
Keyword(s):  
Traffic Flow ◽  
Classification Accuracy ◽  
Euclidean Distance ◽  
Classification Method ◽  
Flow State ◽  
Weight Optimization ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
State Classification ◽  
Fuzzy C Means Clustering

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

Drilling Wear Recognition Based on Fuzzy C-Means Clustering Algorithm

2012 ◽  
Vol 538-541 ◽  
pp. 1408-1412
Author(s):  
Ming Xia Yan
Keyword(s):  
Clustering Algorithm ◽  
Fuzzy C Means ◽  
Degree Of Membership ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Simulation Results ◽  
Severe Degree ◽  
Fuzzy Description ◽  
Matlab Programming ◽  
Wear Modes

Fuzzy c-means clustering algorithm was introduced in detail to classify a set of original sampling data on drilling wear in this paper. Simulation results by Matlab programming show that drill wear modes can be successfully represented by four fuzzy grades after fuzzy clustering and classification. The analysis result indicates that fuzzy description can properly reflect drill wear, FCM can effectively identify different wear modes. It is suggested that the severe degree of membership of wear be used as a criterion for replacement of a drill. This technique is simple and is adaptable to different environment in automatic manufacturing

scholarly journals ANALISIS KECENDERUNGAN LAPORAN MASYARAKAT PADA “LAPORGUB..!” PROVINSI JAWA TENGAH MENGGUNAKAN TEXT MINING DENGAN FUZZY C-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 544-553
Author(s):  
Ratna Kurniasari ◽  
Rukun Santoso ◽  
Alan Prahutama
Keyword(s):  
Text Mining ◽  
Cluster Center ◽  
Text Data ◽  
Fuzzy C Means ◽  
Word Cloud ◽  
Silhouette Coefficient ◽  
Degree Of Membership ◽  
Fuzzy C Means Clustering ◽  
Hard Clustering ◽  
The Government

Effective communication between the government and society is essential to achieve good governance. The government makes an effort to provide a means of public complaints through an online aspiration and complaint service called “LaporGub..!”. To group incoming reports easier, the topic of the report is searched by using clustering. Text Mining is used to convert text data into numeric data so that it can be processed further. Clustering is classified as soft clustering (fuzzy) and hard clustering. Hard clustering will divide data into clusters strictly without any overlapping membership with other clusters. Soft clustering can enter data into several clusters with a certain degree of membership value. Different membership values make fuzzy grouping have more natural results than hard clustering because objects at the boundary between several classes are not forced to fully fit into one class but each object is assigned a degree of membership. Fuzzy c-means has an advantage in terms of having a more precise placement of the cluster center compared to other cluster methods, by improving the cluster center repeatedly. The formation of the best number of clusters is seen based on the maximum silhouette coefficient. Wordcloud is used to determine the dominant topic in each cluster. Word cloud is a form of text data visualization. The results show that the maximum silhouette coefficient value for fuzzy c-means clustering is shown by the three clusters. The first cluster produces a word cloud regarding road conditions as many as 449 reports, the second cluster produces a word cloud regarding covid assistance as many as 964 reports, and the third cluster produces a word cloud regarding farmers fertilizers as many as 176 reports. The topic of the report regarding covid assistance is the cluster with the most number of members. 

scholarly journals APLIKASI K-MEANS DAN FUZY CLUSTERING DALAM PENGELOMPOKAN KECAMATAN DI KABUPATEN BANYUMAS

2021 ◽  
Vol 13 (2) ◽  
pp. 113
Author(s):  
Jajang Jajang ◽  
Nunung Nurhayati ◽  
Yhenis Apriliana
Keyword(s):  
Health Workers ◽  
Health Facilities ◽  
Total Variance ◽  
Variance Ratio ◽  
Clustering Methods ◽  
Cluster Membership ◽  
Fuzzy C Means ◽  
Degree Of Membership ◽  
Better Than

Clustering N objects into c clusters can be used to get information about data observation. Among the clustering methods are K-Means (KMC) and Fuzzy C-means (FCM) methods. In the K-means method, objects are members or not members of the cluster, while in the FCM method, objects are included in the cluster based on the degree of membership. This study discusses the implementation of KMC and FCM in the custering of sub-districts in Banyumas Regency based on total of population, the number of health workers and the number of health facilities and infrastructure. The results showed that the KMC and FCM methods produced the same cluster membership. Furthermore, the analysis of clustering based on the number of population, the number of health workers and the number of health facilities and infrastructure (scenario 1) and based on the number of health workers and the number of health facilities and infrastructure which have been corrected by population (scenario 2). The percentage of the variance ratio between clusters to the total variance in scenario 1 is 69% while in scenario 2 it is 85%. Clustering based on scenario 2 is better than scenario 1.

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