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.

Metric-Based Semi-Supervised Fuzzy C-Means Clustering

2011 ◽  
Vol 268-270 ◽  
pp. 166-171
Author(s):  
Xue Song Yin ◽  
Qi Huang ◽  
Liang Ming Li
Keyword(s):  
Real World ◽  
Side Information ◽  
Data Sets ◽  
Membership Degree ◽  
Real World Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Classification Tasks ◽  
Fuzzy C Means Algorithm

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

Construct Concept Structure for Linear Algebra Based on Cognition Diagnosis and Clustering with Mahalanobis Distances

2011 ◽  
Vol 211-212 ◽  
pp. 756-760
Author(s):  
Chin Chun Chen ◽  
Yuan Horng Lin ◽  
Jeng Ming Yih ◽  
Shu Yi Juan
Keyword(s):  
Linear Algebra ◽  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithms ◽  
Knowledge Structure ◽  
Interpretive Structural Modeling ◽  
Mahalanobis Distances ◽  
Fuzzy C Means ◽  
Euclidean Distance Function ◽  
Fuzzy C Means Algorithm

Euclidean distance function based fuzzy clustering algorithms can only be used to detect spherical structural clusters. The purpose of this study is improved Fuzzy C-Means algorithm based on Mahalanobis distance to identify concept structure for Linear Algebra. In addition, Concept structure analysis (CSA) could provide individualized knowledge structure. CSA algorithm is the major methodology and it is based on fuzzy logic model of perception (FLMP) and interpretive structural modeling (ISM). CSA could display individualized knowledge structure and clearly represent hierarchies and linkage among concepts for each examinee. Each cluster of data can easily describe features of knowledge structures. The results show that there are five clusters and each cluster has its own cognitive characteristics. In this study, the author provide the empirical data for concepts of linear algebra from university students. To sum up, the methodology can improve knowledge management in classroom more feasible. Finally, the result shows that Algorithm based on Mahalanobis distance has better performance than Fuzzy C-Means algorithm.

Sistem Referensi Pemilihan Smartphone Android Dengan Metode Fuzzy C-Means dan TOPSIS

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Giovan Meidy Susanto ◽  
Sandy Kosasi ◽  
David David ◽  
Gat Gat ◽  
Susanti M. Kuway
Keyword(s):  
Software Design ◽  
Euclidean Distance ◽  
Programming Models ◽  
Extreme Programming ◽  
Cluster Center ◽  
Acceptance Testing ◽  
Well Testing ◽  
Test Results ◽  
Fuzzy C Means ◽  
White Box Testing

Difficulties faced by STMIK Pontianak students while choosing an Android smartphone are the diversity of brands, types, series and specifications makes it hard to choose which is the best. Determine that matches to needs along with an appropriate budget also difficult. This research aims to design a reference system for selecting an Android smartphone to resolve the problem. This system was developed using the Fuzzy C-Means algorithm and TOPSIS. The research method used is survey. Software design uses agile with extreme programming models also White-Box Testing, cluster center testing, and acceptance testing. This research found a method to get an alternative group that matches to the user from existing cluster by Euclidean Distance. This research produces a system that can clustering smartphone data and provide references in the form of alternatives that matches to the user. The test results using White-Box Testing produce all functions running well. Testing the cluster center using MSE gets the central error values ​​C1: 1.8481, C2: 2.5316, and C3: 1.8214. Acceptance tesing results above 70%. Weaknesses in this system do not discuss lifestyle needs in choosing an Android smartphone. The criteria used in this research is still technical and not use non-technical criteria.

Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

2019 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Rahadian Kurniawan ◽  
Izzati Muhimmah ◽  
Arrie Kurniawardhani ◽  
Sri Kusumadewi
Keyword(s):  
Microscopic Analysis ◽  
High Accuracy ◽  
Kappa Coefficient ◽  
Field Of View ◽  
Resistant Bacteria ◽  
Screening Process ◽  
Watershed Transformation ◽  
Fuzzy C Means ◽  
Primary Health ◽  
Manual Calculation

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.

scholarly journals Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 696
Author(s):  
Haipeng Chen ◽  
Zeyu Xie ◽  
Yongping Huang ◽  
Di Gai
Keyword(s):  
Brain Tumor ◽  
Information Transfer ◽  
Magnetic Resonance Images ◽  
Low Noise ◽  
Similarity Measurement ◽  
Noise Sensitivity ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Membership Information ◽  
Fuzzy C Means Algorithm

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.

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