scholarly journals Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents

2021 ◽  
Vol 6 (1) ◽  
pp. 79
Author(s):  
Fajar Rohman Hariri
Keyword(s):  
Text Mining ◽  
Islamic Law ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Silhouette Coefficient ◽  
Best Value ◽  
Fuzzy C Means Clustering

Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text. One method of extracting text is Clustering. The present study implements the Fuzzy C-Means Clustering method in MUI fatwa documents to classify existing fatwas based on the similarity of the issues discussed. Silhouette Coefficient is used to analyze the resulting clusters, with the best value of 0.0982 with 10 clusters grouping. Classify fatwas based on the similarity of the issues discussed can make it easier and faster in the search for an Islamic law in Indonesia.

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. 

New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

2019 ◽  
Vol 78 ◽  
pp. 324-345 ◽  
Author(s):  
Mahdi Hashemzadeh ◽  
Amin Golzari Oskouei ◽  
Nacer Farajzadeh
Keyword(s):  
Clustering Method ◽  
Fuzzy C Means ◽  
Feature Weight ◽  
Fuzzy C Means Clustering ◽  
Weight Learning

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 Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Anbu ◽  
Thangavelu ◽  
Ashok
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Rule ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

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