Semi-supervised Fuzzy Clustering Algorithm Research Articles

Electrical Sensor Calibration by Fuzzy Clustering with Mandatory Constraint.

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process.

Semi-supervised fuzzy C means based on membership integration mechanism and its application in brain infarction lesion segmentation in DWI images

In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.

Interclass Balance Factor-based Membership Fusion Semi-supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

Interclass Balance Factor-based Membership Fusion Semi-supervised Fuzzy Clustering Algorithm for Lesion Segmentation in Cerebral Infarction Images

Semi-supervised fuzzy clustering algorithm based on prior membership degree matrix with expert preference

Existing pre-processing methods for the prior membership degree matrix suffer from the following issues: (1) The labeling constraints for prior membership degree matrix have an effect on the expert’s judgment on the prior membership degree, which easily causes the distortion problem of the prior membership degree labeling information; (2) There exists the problem of inconsistency between the filling information and the labeling information in the prior membership degree matrix to be filled in the missing values with zeros. To address these problems, we propose an unconstrained labeling idea for the prior membership degree matrix and the corresponding pre-processing method for the missing values by introducing the statistical characteristics of extreme value distribution and simultaneously apply it to the semi-supervised fuzzy clustering algorithm. More specifically, we focus on learning an expert preference value from the prior membership degree matrix and filling in the missing values with the expert preference value. Thus, we propose an unconstrained pre-processing method for the prior membership degree matrix by filling in missing values with an expert preference to keep the filling information consistent with the labeling information in the prior membership degree matrix as much as possible. In addition, we design a semi-supervised fuzzy clustering algorithm based on an unconstrained prior membership degree matrix with expert preference (SFCM-EP) by introducing the K-L divergence to improve the applicability, utility and running performance of semi-supervised fuzzy clustering algorithm. Our experimental results on the simulation dataset and the UCI datasets show the feasibility and effectiveness of the proposed pre-processing method of the prior membership degree matrix with encouraging results.

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large, accurately labeled medical image datasets are difficult to obtain, traditional clustering algorithms are widely used in medical image segmentation as an unsupervised model. Traditional unsupervised clustering algorithms have limited learning knowledge. Moreover, some semi-supervised fuzzy clustering algorithms cannot fully mine the information of labeled samples, which results in insufficient supervision. When faced with complex mammography images, the above algorithms cannot accurately segment lesion areas. To address this, a semi-supervised fuzzy clustering based on knowledge weighting and cluster center learning (WSFCM_V) is presented. According to prior knowledge, three learning modes are proposed: a knowledge weighting method for cluster centers, Euclidean distance weights for unlabeled samples, and learning from the cluster centers of labeled sample sets. These strategies improve the clustering performance. On real breast molybdenum target images, the WSFCM_V algorithm is compared with currently popular semi-supervised and unsupervised clustering algorithms. WSFCM_V has the best evaluation index values. Experimental results demonstrate that compared with the existing clustering algorithms, WSFCM_V has a higher segmentation accuracy than other clustering algorithms, both for larger lesion regions like tumor areas and for smaller lesion areas like calcification point areas.

Medoid based semi-supervised fuzzy clustering algorithms for multi-view relational data

In this paper we present two novel families of semi-supervised multi-view clustering algorithms for relational data, providing relevance weights for views and cluster representatives. The families differ by their cluster representative approach: set-medoids and vector set-medoids. Each family possesses four models, referred to as variants, differing by their weighting approach and the constraints imposed on those weights. Semi-supervision is attained through the use of must-link and cannot-link pairwise constraints. The algorithms perform the clustering task through the minimisation of an objective function; such minimisation is achieved through the iteration of three steps until convergence, namely: search for the best representatives, computation of the best relevance weights, and computation of the best fuzzy partition. Experiments performed on several real datasets show that those algorithms have greater robustness than other semi-supervised multi-view fuzzy clustering algorithms when handling the pairwise constraints and provide better interpretability of the results. Moreover, those experiments reveal that one family may perform better than the other depending on the data being considered; this can also be observed for the variants within each family. Furthermore, the proposed algorithms were tested in a real multi-view dataset where a synthetic noise view was added; it was observed that those algorithms managed to maintain a good level of quality in the resulting partition as well as discriminating the noise view by assigning it a lower relevance weight.

Application of artificial fish swarm optimization semi-supervised kernel fuzzy clustering algorithm in network intrusion

For the unsupervised learning based clustering algorithm, the intrusion detection rate is low, and the training sample based on supervised learning clustering algorithm is insufficient. A semi-supervised kernel fuzzy C-means clustering algorithm based on artificial fish swarm optimization (AFSA-KFCM) is proposed. Firstly, the kernel function is used to change the distance function in the traditional semi-supervised fuzzy C-means clustering algorithm to define a new objective function, thus improving the probabilistic constraints of the fuzzy C-means algorithm. Then, the artificial fish swarm algorithm with strong global optimization ability is used to improve the KFCM sensitivity to the initial cluster center and easy to fall into the local extremum, thus improving the convergence speed and improving the classification effect. The test results in the Wine and IRIS public datasets show that the AFSA-KFCM clustering algorithm is superior to the traditional algorithm in clustering accuracy and time efficiency. At the same time, the experimental results in KDDCUP99 experimental data show that the algorithm can obtain the ideal detection rate and false detection rate in intrusion detection.

Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering

A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification.

Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification

Clustering is used to detect sound structures or patterns in a dataset in which objects positioned within the same cluster exhibit a substantial level of similarity. In numerous clustering problems, patterns is not easily separable due to the highly complex shaped data. In the previous studies, kernel-based methods have exhibited the effectiveness to partition such data. In this paper, we proposed a semi-supervised clustering method based fuzzy c-means algorithm using multiple kernel technique, called SMKFCM, in which the rudimentary centroids are directly used to the calculating process of centroids. The SMKFCM algorithm is on the basis of combining the labeled and unlabeled data together to improve performance. We used the labeled patterns to calculate the centrality of clusters considered as the rudimentary centroids which are added into the objective functions. The SMKFCM algorithm can be applied to both clustering and classification problems. The experimental results show that SMKFCM algorithm can improve significantly the classification accuracy which comes from comparison with a conventional classification or clustering algorithms such as semi-supervised kernel fuzzy c-means (S2KFCM), semi-supervised fuzzy c-means (SFCM) and Self-trained semi-supervised SVM algorithm (PS3VM).

Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints

In this paper, we propose a novel semi-supervised fuzzy clustering algorithm with spatial constraints for dental segmentation from X-ray images. The detailed contributions include: i) Formulating the spatial features of a dental X-ray image in a dental feature database; ii) Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; iii) Solving the model by the Lagrange multiplier method; iv) Determining the additional information for clustering process by mixing optimal results of Fuzzy C-Means with spatial constraints; v) Proposing a novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation. The new algorithm is validated on a real dataset from Hanoi Medical University, Vietnam including 56 dental images. The experimental results reveal that the proposed work has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods. We also suggest the most appropriate values of parameters that should be opted for the algorithm.

An Overview of Semi-Supervised Fuzzy Clustering Algorithms

Fuzzy clustering plays an important role in data-mining, especially in decision making, pattern recognition, etc. There have been many approaches to improve fuzzy clustering performance and quality when it was first introduced by Bezdek. Recently, an approach related to data with sub-information has been most concerned. The idea of this approach combines the advantages of fuzzy C-means method with the benefits of additional information so-called the semi-supervised fuzzy clustering algorithms (SSFC). Through this report, a series of typical SSFC algorithms are presented in brief to give an overview of this approach.

An Overview of Semi-Supervised Fuzzy Clustering Algorithms

An Overview of Semi-Supervised Fuzzy Clustering Algorithms

A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

Dynamic semi-supervised fuzzy clustering for dental X-ray image segmentation: an analysis on the additional function

Dental X-ray image segmentation is a necessary and important process in medical diagnosis, which assists clinicians to make decisions about possible dental diseases of a patient from a dental X-ray image. It is a multi-objective optimization problem which involves basic components of fuzzy clustering, spatial structures of a dental image, and additional information of experts expressed through a pre-defined membership matrix. In our previous work, the authors presented a semi-supervised fuzzy clustering algorithm using interactive fuzzy satisficing named as SSFC-FS for this problem. An important issue of SSFC-FS is that the pre-defined membership matrix is a fixed function in the sense that it uses the same structure and parameters for all dental images. This is a shortcoming of SSFC-FS since each image has its own structure and morphology so that it needs different membership matrices. In this paper, the authors propose another new dynamic semi-supervised fuzzy clustering called SSFC-FSAI that extends SSFC-FS by employing a collection of pre-defined membership matrices for dental images. A procedure to choose a suitable pre-defined membership matrix for a given dental X-ray image is proposed and attached to SSFC-FSAI. Experimental results on a real dataset of 56 dental X-ray images from Hanoi University of Medical in 2014 - 2015 show that SSFC-FSAI has better accuracy than SSFC-FS and the relevant algorithms.

A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation

Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

Leaf classification using multiple feature analysis based on semi-supervised clustering

Multiple features such as the margin, the shape and the texture of plant leaves are of great importance for classification of plant species, as they are often regarded as the unique features to identify plants. In this paper, we study the performance of a recently proposed semi-supervised fuzzy clustering algorithm with feature discrimination for leaf classification, based on features generated by principal component analysis of color images. The method outlines a basic framework for judging the weights of different features by adopting multiple feature matrixes obtained from the initial images as input data and the clustering results of the proposed clustering algorithm as output data to distinguish dissimilarities between various leaves. Real leaf images are employed to evaluate its performance and the experiment demonstrates that these results suggest that the margin feature, the shape feature and combination feature especially the margin feature and combination feature may be the best choice for leaf classification.

Semi-Supervised Fuzzy Clustering with Feature Discrimination.

Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to obtain a single set of features or weights by weighting and discriminating the information provided by the user. By taking pairwise constraints into account, we propose a semi-supervised fuzzy clustering algorithm with feature discrimination (SFFD) incorporating a fully adaptive distance function. Experiments on several standard benchmark data sets demonstrate the effectiveness of the proposed method.

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

Kernal Based Semi-Supervised Clustering and its Application in Leave Recognition of Bauhinia Blakeana Leaves

A novel kernel based semi-supervised fuzzy clustering algorithm is proposed, and its iterative formula is given. This new algorithm can effectively improve the efficiency of the clustering algorithm. Combined with Fisher projection algorithm, two principal components are extracted from 7 hue statistics and 11 green value statistics, this new semi-supervised clustering method is applied to recognize the angular leaf spot disease of Bauhinia blakeana. The results showed that the consistent rate is 100% for the labeled leaves, and above 95% for other unlabeled leaves.

A Semi-Supervised Fuzzy Clustering Algorithm Based on Mahalanobis Distance and Gaussian Kernel

Semi-supervised clustering is a method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering which introduces Mahalanobis distance and Gaussian Kernel. And we obtain a new semi-supervised fuzzy clustering objective function. By solving the optimization problem, we propose a semi-supervised fuzzy clustering algorithm F-SCAPC which includes F(M)-SCAPC and F(K)-SCAPC. And we do experimental research for proposed algorithm F-SCAPC using the selected standard data set and the artificial data set. Besides, we compare performance of presented algorithm F-SCAPC with AFFC, KFCM-F and SCAPC algorithms. From the results, we can see that F-SCAPC is effective in the convergence speed and the accuracy.