scholarly journals Generalized Fuzzy C-Means Clustering with Improved Fuzzy Partitions and Shadowed Sets

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Seyed Mohsen Zabihi ◽  
Mohammad-R Akbarzadeh-T
Keyword(s):  
Distance Measure ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Clustering Method ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Fuzzy Partitions ◽  
Shadowed Sets

Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.

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.

scholarly journals Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 158
Author(s):  
Tran Dinh Khang ◽  
Nguyen Duc Vuong ◽  
Manh-Kien Tran ◽  
Michael Fowler
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Clustering Method ◽  
Machine Learning Technique ◽  
Practical Applications ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Learning Technique ◽  
Fuzzy C Means Clustering

Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods.

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.  

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.

A Novel Fuzzy Clustering Method with No Outliers Influence

2013 ◽  
Vol 300-301 ◽  
pp. 735-739 ◽  
Author(s):  
Li Jen Kao ◽  
Yo Ping Huang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Human Robot Interaction ◽  
Hand Gesture ◽  
Clustering Method ◽  
Robot Interaction ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy Clustering Method ◽  
Fcm Clustering

Fuzzy C-Means (FCM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FCM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FCM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FCM algorithm can get more accurate centers than the traditional FCM algorithm.

Improved Fuzzy FCM-LI Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 670-673
Author(s):  
Li Bo Hou
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Feature Analysis ◽  
Cluster Center ◽  
High Dimensional ◽  
Fuzzy C Means ◽  
Sample Data ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering ◽  
Np Hard Problem

Fuzzy C-means (FCM) clustering algorithm is one of the widely applied algorithms in non-supervision of pattern recognition. However, FCM algorithm in the iterative process requires a lot of calculations, especially when feature vectors has high-dimensional, Use clustering algorithm to sub-heap, not only inefficient, but also may lead to "the curse of dimensionality." For the problem, This paper analyzes the fuzzy C-means clustering algorithm in high dimensional feature of the process, the problem of cluster center is an np-hard problem, In order to improve the effectiveness and Real-time of fuzzy C-means clustering algorithm in high dimensional feature analysis, Combination of landmark isometric (L-ISOMAP) algorithm, Proposed improved algorithm FCM-LI. Preliminary analysis of the samples, Use clustering results and the correlation of sample data, using landmark isometric (L-ISOMAP) algorithm to reduce the dimension, further analysis on the basis, obtained the final results. Finally, experimental results show that the effectiveness and Real-time of FCM-LI algorithm in high dimensional feature analysis.

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