scholarly journals Neural Network Based Fuzzy C-MEANS Clustering Algorithm

2011 ◽  
pp. 100-104
Author(s):  
Suneetha Chittinen ◽  
Dr. Raveendra Babu Bhogapathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Fast Learning ◽  
Presentation Sequence ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural

In this paper, fuzzy c-means algorithm uses neural network algorithm is presented. In pattern recognition, fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms to group the high dimensional data into clusters. The proposed work involves two steps. First, a recently developed and Enhanced Kmeans Fast Leaning Artificial Neural Network (KFLANN) frame work is used to determine cluster centers. Secondly, Fuzzy C-means uses these cluster centers to generate fuzzy membership functions. Enhanced K-means Fast Learning Artificial Neural Network (KFLANN) is an algorithm which produces consistent classification of the vectors in to the same clusters regardless of the data presentation sequence. Experiments are conducted on two artificial data sets Iris and New Thyroid. The result shows that Enhanced KFLANN is faster to generate consistent cluster centers and utilizes these for elicitation of efficient fuzzy memberships.

scholarly journals AN EFFICIENTFUZZY C-MEANS CLUSTERING ALGORITHM FOR MULTI-VALUED DATA SETS

2021 ◽  
Vol 9 (1) ◽  
pp. 1250-1264
Author(s):  
P Gopala Krishna, D Lalitha Bhaskari
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Similar Data ◽  
Job Descriptions ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Ship Function ◽  
Partition Clustering ◽  
Diverse Data

In data analysis, items were mostly described by a set of characteristics called features, in which each feature contains only single value for each object. Even so, in existence, some features may include more than one value, such as a person with different job descriptions, activities, phone numbers, skills and different mailing addresses. Such features may be called as multi-valued features, and are mostly classified as null features while analyzing the data using machine learning and data mining techniques.  In this paper, it is proposed a proximity function to be described between two substances with multi-valued features that are put into effect for clustering.The suggested distance approach allows iterative measurements of the similarities around objects as well as their characteristics. For facilitating the most suitable multi-valued factors, we put forward a model targeting at determining each factor’s relative prominence for diverse data extracting problems. The proposed algorithm is a partition clustering strategy that uses fuzzy c- means clustering for evolutions, which is using the novel member ship function by utilizing the proposed similarity measure. The proposed clustering algorithm as fuzzy c- means based Clustering of Multivalued Attribute Data (FCM-MVA).Therefore this becomes feasible using any mechanisms for cluster analysis to group similar data. The findings demonstrate that our test not only improves the performance the traditional measure of similarity but also outperforms other clustering algorithms on the multi-valued clustering framework.  

Fruit Fly-Based Artificial Neural Network Classifier with Kernel-Based Fuzzy c-Means Clustering for Satellite Image Classification

2020 ◽  
Vol 20 (02) ◽  
pp. 2050016 ◽  
Author(s):  
Sandeep Kumar ◽  
L. Suresh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Image Classification ◽  
Satellite Image ◽  
Fruit Fly ◽  
Fuzzy C Means ◽  
Satellite Image Classification ◽  
Artificial Neural Network Classifier ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural

Image segmentation and classification are the major challenges to satellite imagery. Also, the identification of unique objects in the satellite image is a significant aspect in the application of remote sensing. Many satellite image classification techniques have been presented earlier. However, the accuracy of the image classification has to be further improved. So that, optimal artificial neural network with kernel-based fuzzy c-means ([Formula: see text]) clustering based satellite image classification is proposed in this paper. Initially, the images are segmented with the help of KFCM algorithm. Then, color features and gray level co-occurrence matrix (GLCM) features to be extracted from the segmented regions. Then, these extracted features are given to the OANN classifier. Based on these features, segmented regions are classified as building, road, shadow, and tree. To enhance the performance of the classifier, the weight values are optimally selected with the help of fruit fly algorithm. Simulation results show that the performance of proposed classifier outperforms that of the existing filters in terms of accuracy.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Sign in / Sign up

Export Citation Format

Share Document