scholarly journals Optimized Fuzzy Cmeans – Fuzzy Covariance – Fuzzy Maximum Likelihood Estimation Clustering Method Based on Deferential Evolutionary Optimization Algorithm for Identification of Rock Mass Discontinuities Sets

2019 ◽  
Author(s):  
Akbar Esmaeilzadeh ◽  
Kourosh Shahriar
Keyword(s):  
Rock Mass ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Field Data ◽  
Likelihood Estimation ◽  
Clustering Methods ◽  
Clustering Method ◽  
Advantages And Disadvantages ◽  
Differential Evolutionary Algorithm ◽  
Clustering Quality

Detecting of joint sets (clusters) is one of the most important processes in determining properties of fractures. Joints clustering and consequently, determination of the mean value representing each cluster is applicable to most rock mass studies. It is clear that the accuracy of the clustering process plays a key role in analyzing stability of infrastructures such as dams and tunnels and so on. Hence, in this paper, by reviewing several methods proposed for clustering fractures and considering their advantages and disadvantages, a three-stage hybrid method is developed which contains Fuzzy c-means, Fuzzy covariance and Fuzzy maximum likelihood estimation that by utilizing the modified orientation matrix had been optimized. This method is optimized by the Differential Evolutionary algorithm using a new and strong cost function which is defined as the computation core. In addition, using three clustering quality comparing criteria, the new developed method of differential evolutionary optimized of fuzzy cmeans - fuzzy covariance - fuzzy maximum likelihood estimation clustering method (DEF3) is compared with other base and common methods using field data. After doing the calculations, the developed method by giving the best values for all the criteria provided the best results and good stability in meeting different criteria. The DEF3 method was validated using actual field data which mapped in Rudbar Lorestan dam site. The results revealed that DEF3 acquired the best rank among the other method by getting the value of 0.5721 of Davis-Bouldin criterion, 1403.1 of Calinski-Harabasz criterion, and 0.83482 of Silihotte as comparing criteria of clustering methods.

scholarly journals Segmentasi Buah Mangga Menggunakan MLE dan GMM Sebagai Klasterisasi Pixel

2020 ◽  
Vol 1 (1) ◽  
pp. 57-67
Author(s):  
Steven Pranata ◽  
Derry Alamsyah
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Error Rate ◽  
Data Distribution ◽  
Likelihood Estimation ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Good Cluster ◽  
Cluster Quality

 Segmentation divides an image into parts or segments that are simpler and more meaningful so they can be analyzed further. The solution that has been found is using the Maximum Likelihood Estimation (MLE) method and the Gausian Mixture Model. GMM is a clustering method. GMM is a function consisting of several Gaussian, each identified by k ∈ {1, ..., K}, where K is the number of clusters in our dataset. Maximum Likelihood estimation is a technique used to find a certain point to maximize a function, this technique is very widely used in estimating a data distribution parameter. Tests carried out using mango images with 10 different backgrounds. GMM will cluster the pixels of the mango image to produce averages and covariates. Then the average and covariance will be used by MLE to qualify each pixel of the mango image. In this study GMM and MLE tests were carried out to segment mangoes. Based on the results obtained, the GMM and MLE methods have  an error rate of 13.07% for 3 clusters, 8.06% for 4 clusters, and 6.63% for 5 clusters and good cluster quality with silhouette coefficient values ​​of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.

scholarly journals Indo-European phylogenetics with R

2020 ◽  
Vol 8 (1) ◽  
pp. 110-180
Author(s):  
David Goldstein
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Computational Methods ◽  
Maximum Parsimony ◽  
Likelihood Estimation ◽  
Phylogenetic Inference ◽  
Neighbor Joining ◽  
Advantages And Disadvantages ◽  
Phylogenetic Methods ◽  
Phylogenetic Estimation

Abstract The last twenty or so years have witnessed a dramatic increase in the use of computational methods for inferring linguistic phylogenies. Although the results of this research have been controversial, the methods themselves are an undeniable boon for historical and Indo-European linguistics, if for no other reason than that they allow the field to pursue questions that were previously intractable. After a review of the advantages and disadvantages of computational phylogenetic methods, I introduce the following methods of phylogenetic inference in R: maximum parsimony; distance-based methods (UPGMA and neighbor joining); and maximum likelihood estimation. I discuss the strengths and weaknesses of each of these methods and in addition explicate various measures associated with phylogenetic estimation, including homoplasy indices and bootstrapping. Phylogenetic inference is carried out on the Indo-European dataset compiled by Don Ringe and Ann Taylor, which includes phonological, morphological, and lexical characters.

Sign in / Sign up

Export Citation Format

Share Document