Determination of Number of Clusters for Fuzzy C-means Maximized with Tsallis Entropy

2021 ◽  
pp. 446-456
Author(s):  
Kazumasa Tamada ◽  
Makoto Yasuda
Keyword(s):  
Tsallis Entropy ◽  
Number Of Clusters ◽  
Fuzzy C Means

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.

scholarly journals Determination of Fertility-Related Traits in Muscadine Grape Population

Plants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1175
Author(s):  
Jiovan Campbell ◽  
Pranavkumar Gajjar ◽  
Ahmed Ismail ◽  
Fariborz Habibi ◽  
Ahmed G. Darwish ◽  
...  
Keyword(s):  
Fruit Set ◽  
The Other ◽  
Average Yield ◽  
Number Of Clusters ◽  
Breeding Lines ◽  
Muscadine Grape

In this study, fertility-related traits of 90 muscadine grape genotypes were evaluated. Selected genotypes included 21 standard cultivars, 60 breeding lines, and nine Vitis × Muscadinia hybrids (VM hybrids). The first fruiting bud (FFB), bud fertility (BF), bud fertility coefficient (BFC), number of flowers/flower cluster (N.F/FC), fruit-set efficiency (FSE), number of clusters/vine (N.C/V), and yield/vine (Y/V) traits were evaluated. The FFB trait did not show significant differences among genotypes. The muscadine genotype O28-4-2-2 (1.6 ± 0.2) displayed the FFB closest to the base; however, O17-16-2-1, O18-2-1, and VM A12-10-2 genotypes had the most distant FFB (3.6 ± 0.3). All the other fertility-related traits varied widely among the population. The BF, BFC, N.F/FC, FSE, N.C/V, and Y/V exhibited a range estimated at 35.1%, 81.5%, 259.7, 63.3%, 177 C/V, and 22.3 kg/V, respectively. The muscadine genotypes O42-3-1 (36.7% ± 1.3) and Majesty (34% ± 1.2) exhibited the highest BF; however, the VM A12-10-2 (1.6% ± 0.1) recorded the lowest BF. The VM genotype O15-16-1 (82.8% ± 4.1) displayed the highest BFC; however, the VM A12-10-2 (1.3% ± 0.1) showed the lowest BFC. The muscadine genotypes D7-1-1 (280.3 F/FC ± 21.7) and O17-17-1 (20.7 F/FC ± 5.5) showed the highest and lowest N.F/FC, respectively. The maximum and minimum FSE was observed for the Rosa cultivar (65.7% ± 2.4) and muscadine genotype D7-1-1 (2.4% ± 0.2), respectively. The minimum N.C/V was recorded for VM genotype A12-10-2 (6 C/V ± 0.2) and maximum noted for muscadine genotypes B20-18-2 (183 C/V ± 7.5) and O44-14-1 (176 C/V ± 7.3). Muscadine genotype O23-11-2 (22.6 kg ± 1.1) produced the highest Y/V; however, the lowest yield was recorded for O15-17-1, Fry Seedless, Sugargate, and the VM genotypes and A12-10-2, with an average yield among them estimated at 0.4 kg ± 0.2.

scholarly journals Finding the Number of Clusters in Data and Better Initial Centers for K-means Algorithm

2020 ◽  
Vol 12 (6) ◽  
pp. 1-20
Author(s):  
Ahmed Fahim ◽  
Keyword(s):  
Data Clustering ◽  
Linear Time ◽  
Original Data ◽  
Local Minima ◽  
Expected Number ◽  
Open Problems ◽  
Number Of Clusters ◽  
Benchmark Datasets ◽  
Selection Of

The k-means is the most well-known algorithm for data clustering in data mining. Its simplicity and speed of convergence to local minima are the most important advantages of it, in addition to its linear time complexity. The most important open problems in this algorithm are the selection of initial centers and the determination of the exact number of clusters in advance. This paper proposes a solution for these two problems together; by adding a preprocess step to get the expected number of clusters in data and better initial centers. There are many researches to solve each of these problems separately, but there is no research to solve both problems together. The preprocess step requires o(n log n); where n is size of the dataset. This preprocess step aims to get initial portioning of data without determining the number of clusters in advance, then computes the means of initial clusters. After that we apply k-means on original data using the resulting information from the preprocess step to get the final clusters. We use many benchmark datasets to test the proposed method. The experimental results show the efficiency of the proposed method.

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.

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