Weighted Fuzzy Clustering Algorithm Research Articles

A Feature Weighted Fuzzy Clustering Algorithm Based on Multistrategy Grey Wolf Optimization

Traditional fuzzy clustering is sensitive to initialization and ignores the importance difference between features, so the performance is not satisfactory. In order to improve clustering robustness and accuracy, in this paper, a feature-weighted fuzzy clustering algorithm based on multistrategy grey wolf optimization is proposed. This algorithm cannot only improve clustering accuracy by considering the different importance of features and assigning each feature different weight but also can easily obtain the global optimal solution and avoid the impact of the initialization process by implementing multistrategy grey wolf optimization. This multistrategy optimization includes three components, a population diversity initialization strategy, a nonlinear adjustment strategy of the convergence factor, and a generalized opposition-based learning strategy. They can enhance the population diversity, better balance exploration and exploitation, and further enhance the global search capability, respectively. In order to evaluate the clustering performance of our clustering algorithm, UCI datasets are selected for experiments. Experimental results show that this algorithm can achieve higher accuracy and stronger robustness.

An Automatic Magnetic Resonance Brain Image Segmentation Method Using a Multitask Weighted Fuzzy Clustering Algorithm

In clinical assisted diagnosis, it is an important way to obtain information with the help of medical images. Qualitative and quantitative analysis of brain tissue has become a research hotspot for brain diseases. Therefore, image segmentation technology is an indispensable link in medical image analysis. Due to the defects such as ambiguity, complexity, gray-scale unevenness, partial volume effect in magnetic resonance brain images, it is essential to improve the segmentation performance of classical algorithms in medical images. In this paper, multitasking and weighted fuzzy clustering algorithm are combined as a new algorithm (MT-WFCM) for MRI brain image segmentation. The proposed MT-WFCM algorithm improve the clustering performance of all tasks through common information between different magnetic resonance brain images with correlation. Besides, the difference between MT-WFCM and MT-FCM is that task weights are added to avoid negative effects between tasks in the segmentation process. According to five different comparative experiments, the MT-WFCM algorithm can mine the cooperative relationship among each task and the characteristics of each task effectively. In magnetic resonance image (MRI) segmentation, multi-task weighted fuzzy c-means clustering method can make up for the shortcomings of single-task clustering algorithm, strengthen the relationship between tasks, and get more accurate segmentation results.

Classification of Ship’ Magnetic Field and Feature Selection Based on the Improved Weighted Fuzzy Clustering Algorithm

Magnetic field data of ship has three-component,and traditional weighted fuzzy clustering algorithm(FCA) can’t deal with the three-component data. We improve the traditional FCA by changing the objective function and added weights calculation of three-component of magnetic field in the function.Give the equation to compute the weights of three-component.Put forward new steps for improved algorithm.Use ships’ data to test the improved algorithm and giving the conclusion.

An Adaptive Weighted Fuzzy C-means Clustering Algorithm for Remote Sensing Image Classification

The Fuzzy C-means (FCM) clustering algorithm is a well-known tool for pattern and image classification, while FCM is showing unstable behaviors with different fuzzy indexes. The New Weighted Fuzzy C-means (NW-FCM) algorithm, based on the weighted mean concept, was proposed to improve the performance of FCM, however, NW-FCM needs man-machine interaction and can not classify by itself. In this paper, an Adaptive Weighted Fuzzy Clustering Algorithm (AWFCM), which integrates the weighted mean concept with the mechanism of splitting and lumping from the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA), is proposed for remote sensing image classification. At the same time, a statistical method is introduced into the algorithm. The AWFCM does not need a priori knowledge about the number of clusters and their centers, and can automatically estimate an initial number of clusters and their centers, also the optimum final number of clusters. Experimental results demonstrate that the AWFCM has better performance than those of the K-means, ISODATA and FCM algorithms.

Research on the Improved Generalized Weighted Fuzzy Clustering Algorithm

Research on the Improved Generalized Weighted Fuzzy Clustering Algorithm

Improved method for the feature extraction of laser scanner using genetic clustering

Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated.