Weighting Exponent Selection of Fuzzy C-Means via Jacobian Matrix

2014 ◽  
pp. 115-126 ◽  
Author(s):  
Liping Jing ◽  
Dong Deng ◽  
Jian Yu
Keyword(s):  
Jacobian Matrix ◽  
Fuzzy C Means ◽  
Selection Of

scholarly journals An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

An Investigation of the Implementation of the p-Version Finite Element Method

1995 ◽  
Author(s):  
Simon D. Campion ◽  
John L. Jarvis
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Function Method ◽  
Jacobian Matrix ◽  
P Elements ◽  
Load Vector ◽  
Geometry Mapping ◽  
Selection Of ◽  
Interpolation Functions ◽  
Element Method

Abstract The use of the p-version finite element method has become more widespread over the last five years or so, as witnessed by the addition of p-elements to a number of well known commercial codes. A review of the keynote papers on the p-version method is presented which focusses on the use of the hierarchical concept and the selection of the interpolation functions. The importance of accurate geometry mapping is also discussed, and the use of the blending function method is presented. Details of implementation of the p-version method are discussed in the light of the authors efforts to develop a program for solving two-dimensional elastostatic problems. Topics covered include the rules for numerical integration for the p-method, the possible use of numerical rather than explicit differentiation for determining the Jacobian matrix, and the programming of the load vector for the p-method. The lessons learnt are illustrated by simple examples, and will be of benefit to those wishing to program p-elements for other applications.

Feature reduction fuzzy C-Means algorithm leveraging the marginal kurtosis measure

2020 ◽  
Vol 39 (5) ◽  
pp. 7259-7279
Author(s):  
Xingguang Pan ◽  
Shitong Wang
Keyword(s):  
Clustering Algorithm ◽  
Feature Reduction ◽  
Feature Weights ◽  
Fuzzy C Means ◽  
Initial Cluster ◽  
Fuzzy C Means Clustering ◽  
Real World Datasets ◽  
Clustering Data ◽  
Selection Of ◽  
Original Feature

The feature reduction fuzzy c-means (FRFCM) algorithm has been proven to be effective for clustering data with redundant/unimportant feature(s). However, the FRFCM algorithm still has the following disadvantages. 1) The FRFCM uses the mean-to-variance-ratio (MVR) index to measure the feature importance of a dataset, but this index is affected by data normalization, i.e., a large MVR value of original feature(s) may become small if the data are normalized, and vice versa. Moreover, the MVR value(s) of the important feature(s) of a dataset may not necessarily be large. 2) The feature weights obtained by the FRFCM are sensitive to the initial cluster centers and initial feature weights. 3) The FRFCM algorithm may be unable to assign the proper weights to the features of a dataset. Thus, in the feature reduction learning process, important features may be discarded, but unimportant features may be retained. These disadvantages can cause the FRFCM algorithm to discard important feature components. In addition, the threshold for the selection of the important feature(s) of the FRFCM may not be easy to determine. To mitigate the disadvantages of the FRFCM algorithm, we first devise a new index, named the marginal kurtosis measure (MKM), to measure the importance of each feature in a dataset. Then, a novel and robust feature reduction fuzzy c-means clustering algorithm called the FRFCM-MKM, which incorporates the marginal kurtosis measure into the FRFCM, is proposed. Furthermore, an accurate threshold is introduced to select important feature(s) and discard unimportant feature(s). Experiments on synthetic and real-world datasets demonstrate that the FRFCM-MKM is effective and efficient.

scholarly journals New Method to Optimize Initial Point Values of Spatial Fuzzy c-means Algorithm

2015 ◽  
Vol 5 (5) ◽  
pp. 1035
Author(s):  
Iman Omidvar Tehrani ◽  
Subariah Ibrahim ◽  
Habib Haron
Keyword(s):  
Medical Image ◽  
Initial Point ◽  
Medical Image Segmentation ◽  
Seed Point ◽  
Fuzzy C Means ◽  
Initial Values ◽  
Optimum Selection ◽  
Segmentation Algorithms ◽  
Previous Algorithm ◽  
Selection Of

Fuzzy based segmentation algorithms are known to be performing well on medical images. Spatial fuzzy C-means (SFCM) is broadly used for medical image segmentation but it suffers from optimum selection of seed point initialization which is done either manually or randomly. In this paper, an enhanced SFCM algorithm is proposed by optimizing the SFCM initial point values. In this method in order to increasing the algorithm speed first the approximate initial values are determined by calculating the histogram of the original image. Then by utilizing the GWO algorithm the optimum initial values could be achieved. Finally By using the achieved initial values, the proposed method shows the significant improvement in segmentation results. Also the proposed method performs faster than previous algorithm i.e. SFCM and has better convergence. Moreover, it has noticeably improved the clustering effect.

scholarly journals Suboptimal approximations in repeatable inverse kinematics for robot manipulators

2017 ◽  
Vol 65 (2) ◽  
pp. 209-217
Author(s):  
I. Duleba ◽  
I. Karcz-Duleba
Keyword(s):  
Configuration Space ◽  
Simulation Study ◽  
Inverse Kinematics ◽  
Jacobian Matrix ◽  
Robot Manipulator ◽  
Inverse Kinematic ◽  
Forward Kinematics ◽  
Definition Of ◽  
Pseudo Inverse ◽  
Selection Of

Abstract In this paper a repeatable inverse kinematic task was solved via an approximation of a pseudo-inverse Jacobian matrix of a robot manipulator. An entry configuration to the task was optimized and a task-dependent definition of an approximation region, in a configuration space, was utilized. As a side effect, a relationship between manipulability and optimally augmented forward kinematics was established and independence of approximation task solutions on rotations in augmented components of kinematics was proved. A simulation study was performed on planar pendula manipulators. It was demonstrated that selection of an initial configuration to the repeatable inverse kinematic task heavily impacts solvability of the task and its quality. Some remarks on a formulation of the approximation task and its numerical aspects were also provided.

scholarly journals OPTIMASI PSO UNTUK METODE CLUSTERING FUZZY C-MEANS DALAM PENGELOMPOKAN KELAS

Petir ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 72-91
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Data Object ◽  
Local Solutions ◽  
Data Objects ◽  
The Impact ◽  
Selection Of

Clustering is a method that divides data objects into groups based on information found in data describing objects and relationships between them. In partition-based cluster analysts K-Means method and Fuzzy CMeans Method which is a frequent and commonly used clustering method. However, in its development now with various macar data with the complexity of the variable would be more asked again to the effectiveness and as efficiently whether the method can cluster the data. So it is necessary to optimize where the method has a weakness that is likely to mask the impact of the deficiency in clustering a data object that has a complex variable. Data data used is the value of slot shift one with a variable of the value Academic, nonacademic value in the form of a questionnaire value attitudes assessed by each teacher will be inputted, as a determinant of the division of leading classes in the future in the next teaching. In this study tried to correct some of the deficiencies of the Fuzzy C-Means algorithm ie the selection of early cluster centers and local solutions. An efficient algorithm is proposed to improve the grouping of such methods with Particle Swarm Optimization. In recent years, Particle Swarm Optimization (PSO) has been successfully applied to a number of real-world grouping issues with fast and effective convergence for high-dimensional data.

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