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Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership...
Image segmentation is one of the most important research areas in image processing and computer vision, and is a key step in image processing and image analysis. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. Based on the measure of medium truth degree, this paper presents a novel image segmentation method by introducing the distance...
The paper adopts the fuzzy c-means text mining method in lots of text mining methods. But aim at the defect that the initial value of the fuzzy c-means is more sensitivity and poor stability, an improved GAFCM text mining method has been put forward. GAFCM uses global search features of genetic algorithms to improve the fuzzy c-means. Finally, it has proved that the improved text mining method has...
In respect to the recognization of oil-well identifier in scanned petroleum geologic structure map, a fuzzy classification & identification method based on matching template correction is presented. By analyzing the attributes of a target map and correcting the matching template, identification & classification is conducted through fuzzy membership algorithm. The experimental results showed...
This paper presents an example-based color alternation algorithm between images. Dissimilar to a well-known conventional color transfer method, our approach takes into consideration the weighted influences of the source as well as the target image. Our algorithm automatically calculates the weights according to a fuzzy value, either derived from the statistical features of the source and target images,...
The key to the implementation of dynamic forensics is how to mine in real-time and effectively criminal invasion information from voluminous data. Towards the disadvantages of Fuzzy C-means clustering (referred to as FCM) forensics analysis that it is very sensitive to initial data and impacted greatly by noise, a dynamic forensics analysis technology based on genetic-fuzzy clustering algorithm is...
The knowledge reduction function of rough sets theory is specific on discrete data, while most attributes of decision tables are continuous. Therefore a global discretization and attribute reduction algorithm is proposed based on clustering and rough sets theory. After comparing different discretization methods, the k-means clustering algorithm is used. In order to avoid the shortcomings of k-means...
Existing clustering ensemble algorithms for partitioning categorical data only apply to know the generating process of clustering members very well. In order to broaden the application of clustering ensemble, a fuzzy clustering ensemble algorithm for partitioning categorical data is proposed in this paper. The proposed algorithm makes use of relationship degree between different attributes for pruning...
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features. To solve the localization of LAC in specifying the number of clusters, this paper reworks the validity index for fuzzy clustering to evaluate the clustering results of LAC. Compared with real clustered data, the method is proved feasible. In the new algorithm,...
Spatial information enhances the quality of clustering which is not utilized in the conventional FCM. Normally fuzzy c-means (FCM) algorithm is not used for color image segmentation and also it is not robust against noise. In this paper, we presented a modified version of fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering of color images...
Image segmentation is one of the key steps in image analysis, where the fuzzy theory based methods are widely used, but, none had universally property to segment all types color images. So, to improve segmentation quality and universally property of segmentation algorithm, a new fuzzy color image segmentation algorithm was brought out based on feature divergence and fuzzy dissimilarity. The algorithm...
The fuzzy c-means method (FCM) is an important research topic that has practical applications in many fields. However, the greatest disadvantage of this method is its sensitivity to initialized values including the number of clusters and cluster centroids. The main goal of this paper is to present an effective method for automatically determining the number of clusters and acquiring the corresponding...
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