Relative Density Weights Based Fuzzy C-Means Clustering Algorithms

Image segmentation plays an important role in machine vision, image recognition, and imaging applications. Based on the fuzzy c-means clustering algorithm, a kernel-distance-based intuitionistic fuzzy c-means clustering (KIFCM) algorithm is proposed. First, a fuzzy complement operator is used to generate the membership degree whereby the hesitation degree of intuitionistic fuzzy set is generated; second, a kernel-induced function is used to calculate the distance from each point to the cluster center instead of the Euclidean distance; third, a new objective function that includes the hesitation degree is established, and the optimization of the objective function results in new iterative expressions for the membership degree and the cluster center. The proposed KIFCM algorithm is compared with the fuzzy c-means clustering (FCM) algorithm, the kernel fuzzy c-means clustering (KFCM) algorithm, and the intuitionistic fuzzy c-means clustering (IFCM) algorithm in segmenting five images. The experimental results verify the effectiveness and superiority of our proposed KIFCM algorithm.

There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the k-nearest neighbor (KNN) algorithm, is proposed to improve the performance of MR image segmentation. In this algorithm, the statistical gray level histogram of image is used in KFCM algorithm to speed up the algorithm. Furthermore, the spatial information of image is also considered by k-nearest neighbor algorithm based on kernel methods. With kernel methods each pixel of image is mapped into a high-dimensional feature space where FCM algorithm and KNN algorithm are carried out. Experiments show that the proposed algorithm is effective and efficient in image segmentation.

Clustering documents enable the user to have a good overall view of the information contained in the documents. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which a data belongs to different clusters. In this system, documents are clustered by using fuzzy c-means (FCM) clustering algorithm. FCM clustering is one of well-know unsupervised clustering techniques. However FCM algorithm requires the user to pre-define the number of clusters and different values of clusters corresponds to different fuzzy partitions. So the validation of clustering result is needed. PBM index and F-measure are used for cluster validity.

Possibilistic fuzzy c-means clustering (PFCM) algorithm improves the noise sensitivity of the fuzzy c-means clustering (FCM) algorithm and the serious coincident clustering phenomenon of the possibilistic c-means clustering (PCM) algorithm. However, the Euclidean distance of the PFCM treats all features equally in the clustering process and doesn't consider the imbalance between sample features, resulting in low clustering accuracy. Furthermore, as the number of clusters increases, the PFCM still has a partially coincident clustering problem due to the lack of between-class relationships of possibilistic memberships. Therefore, a feature weighted cutset-type possibilistic fuzzy c-means clustering (FW-C-PFCM) algorithm is proposed by introducing the cutset theory and feature weights into the PFCM in this paper. Firstly, the proposed FW-C-PFCM introduces a feature weighted parameter in the objective function, and different features should take different weight values according to the distribution of samples. Secondly, the cutset theory is utilized to divide the data into inner core and outer core regions, and some possibilistic memberships inside each cluster core are modified in the iterative process. Finally, the experiments on synthetic multi-class datasets and the Iris dataset show that the FW-C-PFCM improves the partially coincident clustering phenomenon and overcomes the imbalance between sample features. The comparative experiments on a color image also indicate that the FW-C-PFCM enhances the segmentation accuracy and generates better clustering results than PFCM.

Fuzzy C-means (FCM) clustering algorithm is an important and popular clustering algorithm which is utilized in various application domains such as pattern recognition, machine learning, and data mining. Although this algorithm has shown acceptable performance in diverse problems, the current literature does not have studies about how they can improve the clustering quality of partitions with overlapping classes. The better the clustering quality of a partition, the better is the interpretation of the data, which is essential to understand real problems. This work proposes two robust FCM algorithms to prevent ambiguous membership into clusters. For this, we compute two types of weights: an weight to avoid the problem of overlapping clusters; and other weight to enable the algorithm to identify clusters of different shapes. We perform a study with synthetic datasets, where each one contains classes of different shapes and different degrees of overlapping. Moreover, the study considered real application datasets. Our results indicate such weights are effective to reduce the ambiguity of membership assignments thus generating a better data interpretation.

Bias-correction fuzzy clustering algorithms

A fault identification with fuzzy C-Mean clustering algorithm based on improved ant colony algorithm (ACA) is presented to avoid local optimization in iterative process of fuzzy C-Mean (FCM) clustering algorithm and the difficulty in fault classification. In the algorithm, the problem of fault identification is translated to a constrained optimized clustering problem. Using heuristic search of colony can find good solutions. And according to the information content of cluster center, it could merger surrounding data together to cause clustering identification. The algorithm may identify fuzzy clustering numbers and initial clustering center. It can also prevent data classification from causing some errors. Thus, applying in fault diagnosis shows validity of computing and credibility of identification results.

The fuzzy C-means (FCM) algorithm has got significant importance compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by some auxiliary variables and spatial information in the membership functions. By combining these two aspects, we are able to solve the problems of sensitivity to noisy data and inhomogeneity. The experimental results on several simulated and real-patient MRI brain images show that the csFCM method has superior performance on image segmentation than the FCM algorithm and some other FCM-based clustering algorithms.

Gold immunochromatographic strip (GICS) quantitative detective can provide more information than the qualitative or semiquantitative testing. In this paper, a fast color image segmentation method is presented to develop the quantitative detective of GICS. The image of GICS was acquired by Charge-coupled Device (CCD) image sensor, and segmented by fuzzy c-means (FCM) clustering algorithm based on color histogram in HSV color space. Maximin-distance algorithm was adopted to get the initial positions of centroids and cluster number to overcome the shortcoming that the FCM algorithm may produce local optimal results. For the segmented target image, a special characteristic parameter was constructed and calculated in HSV color space to achieve the quantitative interpretation of the GICS.

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FCM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFCMwUNC clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FCM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFCMwUNC clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFCMwUNC clustering algorithm for the symbolic interval-values data.

An image segmentation method based on a modified local-information weighted intuitionistic Fuzzy C-means clustering and Gold-panning Algorithm

In this work the development of an interval type-2 fuzzy clustering algorithm, combining the Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms is presented. The process of data clustering is carried out with a fuzzification exponent of m = 2. The development of the interval fuzzy clustering algorithm with a fixed fuzzification exponent (e.g. m = 2), instead of a fuzzification interval [m1, m2] consists of the combination of the FCM and PCM algorithms. This interval fuzzy clustering algorithm is possible because the computation of the used fuzzy partition matrices for each fuzzy clustering algorithm is different. This was proposed to overcome the disadvantages of not properly managing uncertainty in data clustering.

Many components of intelligent transportation systems require different levels of the traffic flow forecasting. This paper presents a novel short-term traffic flow forecasting model using a distributed radial basis function neural network (RBFNN) based on adaptive fuzzy c-means (FCM) clustering algorithm. FCM clustering algorithm is used to classify training objects into a couple of clusters, each cluster is trained by a sub RBFNN, and membership values are used for combining several RBFNN outputs to obtain the final result. In the online stage, the membership values are computed using an adaptive fuzzy clustering algorithm for the new object. The real traffic data are used to demonstrate the effectiveness of the method.

<p>This study intends to combine the fuzzy c-means clustering and genetic algorithms to cluster the customers of steel industry. The customers were divided into two clusters by using the variables of the LRFM (length, recency, frequency, monetary value) model. Results indicated that customers belonging to the first cluster had a higher length of the relationship, recency of trade, and frequency of trade but lower monetary value compared to the average values of these criteria for all customers. The results also showed that customers belonging to the second<br />cluster had a higher recency of trade and monetary value but lower length of the relationship and frequency of trade compared to the average values of these criteria for all customers. It was also found that the combined algorithm (i.e., fuzzy c-means clustering and genetic algorithm) used in this study had a lower mean squared error (MSE) compared to fuzzy c-means clustering.</p>

Time-of-use price is an important means of demand side management, how to accurately divide peak and valley periods is an important problem to be solved. In this paper, an improved fuzzy c-means (FCM) clustering algorithm is proposed to solve the problem that traditional FCM clustering algorithm is sensitive to the initial clustering center and easy to fall into local optimal. This algorithm combines an improved particle swarm optimization (PSO) based on support vector machine regression (SVR) with FCM algorithm, makes full use of the strong global search ability of PSO, replaces FCM to find the clustering center at the beginning of iteration, makes it out of the local optimal, realizes fuzzy clustering, and thus classifies more accurate peak and valley periods. Finally, the improved algorithm is applied to an example of an industrial park, and a distributed energy storage system is set up. The effectiveness of the proposed method is verified by simulation.