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In clustering methods, the estimation of the optimal number of clusters is significant for subsequent analysis. As a simple clustering method, the fuzzy c-means algorithm (FCM) has been widely discussed and applied in pattern recognition and machine learning. However, the FCM could not guarantee unique clustering result because initial cluster number is chosen randomly. As the number of clusters is...
We present an intelligent approach for understanding user interaction to simplify the interface of graph-based image segmentation. The user draws a set of markers (strokes) over the object and the background, and our method automatically determines a subset of these pixels with dissimilar image properties for arc-weight estimation. Arc-weight estimation combines object information learned from the...
This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal...
In this paper, we present a new Neighbor Sharing Selection based Agglomerative fuzzy K-means (NSS-AKmeans) algorithm for learning optimal number of clusters and generating better clustering results. The NSS-AKmeans can identify high density areas and determine initial cluster centers from these areas with a neighbor sharing selection method. To select initial cluster centers, we propose an agglomeration...
Fuzzy clustering has also been applied to object recognition with certain shapes in image processing. This paper proposes a new approach to grid-like cluster extraction, in which gridlike structures are extracted by extended fuzzy c-lines with local coordinate rotation. In the prototype recognition step, four sides of squares are captured in a similar procedure for linear fuzzy clustering after cluster-wise...
This paper proposes a novel colorization algorithm for monochrome still images based on fuzzy clustering and distance transformation. Given small amount of typical color scribbles manually marked on the input grayscale image, the followed colorization process consists of four main steps: color scribble extraction, distance transformation and spatial weight estimation, fuzzy clustering and luminance...
This paper presents a modified FCM algorithm for segmentation of MRI. The proposed method has introduced by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis. The proposed method can deal with the intensity in-homogeneities and image noise effectively. have compared our results with other reported methods...
Based on fuzzy clustering and multi-model support vector regression, a novel lithium-ion battery state of charge (SOC) estimating model for electric vehicle is proposed. Fuzzy C-means and subtractive clustering combined algorithm is employed to implement the fuzzy partition for the input space with the input vectors sampled in UDDS drive cycle, temperature, current, load voltage of the lithium-ion...
As a non-parametric algorithm, empirical copula is an effective way to estimate the dependence structure of high-dimension arbitrarily distributed data. However, it suffers from the problem of huge computation time because of its high computational complexity. In this paper, fuzzy empirical copula is proposed to solve this problem by combining the fuzzy clustering by local approximation of memberships...
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image...
Gene expression profiling plays an important role in a broad range of areas in biology. The raw gene expression data, may contain missing values. It is an important preprocessing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis. Numerous methods have been developed to deal with missing values. In this paper,...
Data clustering constitutes at present a commonly used technique for extracting fuzzy system rules from experimental data. Detailed studies in the field have shown that using above-mentioned method results in significantly reduced structure of fuzzy identification system, maintaining at the same time its high modelling efficiency. In this paper a clustering algorithm, based on a kernel density gradient...
Fuzzy c-regression models (FCRM) performs switching regression based on a Fuzzy c-means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal...
Linear fuzzy clustering is a fuzzy clustering-based local PCA technique, in which the Fuzzy c-Means (FCM)-like iterative procedure is performed by using linear varieties as the prototypes of clusters. Fuzzy c-Medoids (FCMdd) is a modified FCM algorithm, in which the representative objects ldquomedoidsrdquo are selected from data samples, and is useful for handling relational data. This paper proposes...
This paper introduce a type-2 fuzzy function system for uncertainty modeling using evolutionary algorithms (ET2FF). The type-1 fuzzy inference systems (FISs) with fuzzy functions, which do not entail if...then rule bases, have demonstrated better performance compared to traditional FIS. Nonetheless, the performance of these approaches is usually affected by their uncertain parameters. The proposed...
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