A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data

2021 ◽  
pp. 116153
Author(s):  
Hossein Saberi ◽  
Reza Sharbati ◽  
Behzad Farzanegan
Keyword(s):  
Noisy Data ◽  
Fuzzy C Means ◽  
Gradient Ascent

Generalized entropy based possibilistic fuzzy C-Means for clustering noisy data and its convergence proof

2017 ◽  
Vol 219 ◽  
pp. 186-202 ◽  
Author(s):  
S. Askari ◽  
N. Montazerin ◽  
M.H. Fazel Zarandi ◽  
E. Hakimi
Keyword(s):  
Noisy Data ◽  
Generalized Entropy ◽  
Fuzzy C Means ◽  
Convergence Proof

An effective modified possibilistic Fuzzy C-Means clustering algorithm for noisy data problems

2021 ◽  
Author(s):  
Souad Azzouzi ◽  
Jaouad El-Mekkaoui ◽  
Amal Hjouji ◽  
Ahmed El Khalfi
Keyword(s):  
Clustering Algorithm ◽  
Noisy Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

Noise Fuzzy Learning Vector Quantization

2010 ◽  
Vol 439-440 ◽  
pp. 367-371
Author(s):  
Xiao Hong Wu ◽  
Bin Wu ◽  
Jie Wen Zhao
Keyword(s):  
Vector Quantization ◽  
Noisy Data ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
Fuzzy C Means ◽  
Learning Rates ◽  
Sensitivity Problem ◽  
Noise Clustering ◽  
Fuzzy Learning ◽  
Better Than

Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.

scholarly journals A Generalization of Possibilistic Fuzzy C-Means Method for Statistical Clustering of Data

2021 ◽  
Vol 15 ◽  
pp. 1759-1765
Author(s):  
Souad Azzouzi ◽  
Amal Hjouji ◽  
Jaouad EL- Mekkaoui ◽  
Ahmed EL Khalfi
Keyword(s):  
Euclidean Distance ◽  
Dimensional Space ◽  
Noisy Data ◽  
High Accuracy ◽  
Cluster Center ◽  
Fuzzy C Means ◽  
Clustering And Classification ◽  
The Comparative Study ◽  
Fuzzy C Means Algorithm ◽  
Statistical Clustering

The Fuzzy C-means (FCM) algorithm has been widely used in the field of clustering and classification but has encountered difficulties with noisy data and outliers. Other versions of algorithms related to possibilistic theory have given good results, such as Fuzzy C- Means(FCM), possibilistic C-means (PCM), Fuzzy possibilistic C-means (FPCM) and possibilistic fuzzy C- Means algorithm (PFCM).This last algorithm works effectively in some environments but encountered more shortcomings with noisy databases. To solve this problem, we propose in this manuscript, a new algorithm named Improved Possibilistic Fuzzy C-Means (ImPFCM) by combining the PFCM algorithm with a very powerful statistical method. The properties of this new ImPFCM algorithm show that it is not only applicable on clusters of spherical shapes, but also on clusters of different sizes and densities. The results of the comparative study with very recent algorithms indicate the performance and the superiority of the proposed approach to easily group the datasets in a large-dimensional space and to use not only the Euclidean distance but more sophisticated standards norms, capable to deal with much more complicated problems. On the other hand, we have demonstrated that the ImPFCM algorithm is also capable of detecting the cluster center with high accuracy and performing satisfactorily in multiple environments with noisy data and outliers.

scholarly journals Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

2021 ◽  
Vol 9 (3) ◽  
pp. 618-629
Author(s):  
Hoda Khanali ◽  
Babak Vaziri
Keyword(s):  
Support Vector Machine ◽  
Clustering Algorithm ◽  
Noisy Data ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Fuzzy C Means ◽  
Computational Speed ◽  
Fuzzy C Means Clustering ◽  
High Degree

Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.

Noisy Data: Incorporating Variability into Your Analysis

2014 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Richard Schwartz
Keyword(s):  
Noisy Data
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