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.

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 Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

2020 ◽  
Author(s):  
Mohammad Hossein Olyaee ◽  
Alireza Khanteymoori
Keyword(s):  
High Throughput Sequencing ◽  
Human Genetics ◽  
Conflict Graph ◽  
Single Individual ◽  
Haplotype Reconstruction ◽  
Fuzzy C Means ◽  
Complementary Role ◽  
Fuzzy C Means Clustering ◽  
Probable Relation

AbstractEvolution of human genetics is one of the most interesting areas for researchers. Determination of Haplotypes not only makes valuable information for this purpose but also performs a major role in investigating the probable relation between diseases and genomes. Determining haplotypes by experimental methods is a time-consuming and expensive task. Recent progress in high throughput sequencing allows researchers to use computational methods for this purpose. Although, several algorithms have been proposed but they are less accurate when the error rate of input fragments increases. In this paper, first, a fuzzy conflict graph is constructed based on the similarities of all input fragments and next, the cluster centers are used as initial centers by fuzzy c-means (FCM) algorithm. The proposed method has been tested on several real datasets and compared with some current methods. The comparison with the existing approaches shows that our method can be a complementary role among the others.

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