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1.
Conventional Fuzzy C-means (FCM) algorithm uses Euclidean distance to describe the dissimilarity between data and cluster prototypes. Since the Euclidean distance based dissimilarity measure only characterizes the mean information of a cluster, it is sensitive to noise and cluster divergence. In this paper, we propose a novel fuzzy clustering algorithm for image segmentation, in which the Mahalanobis distance is utilized to define the dissimilarity measure. We add a new regularization term to the objective function of the proposed algorithm, reflecting the covariance of the cluster. We experimentally demonstrate the effectiveness of the proposed algorithm on a generated 2D dataset and a subset of Berkeley benchmark images.  相似文献   

2.
The sources of evidence may have different reliability and importance in real applications for decision making. The estimation of the discounting (weighting) factors when the prior knowledge is unknown have been regularly studied until recently. In the past, the determination of the weighting factors focused only on reliability discounting rule and it was mainly dependent on the dissimilarity measure between basic belief assignments (bba's) represented by an evidential distance. Nevertheless, it is very difficult to characterize efficiently the dissimilarity only through an evidential distance. Thus, both a distance and a conflict coefficient based on probabilistic transformations BetP are proposed to characterize the dissimilarity. The distance represents the difference between bba's, whereas the conflict coefficient reveals the divergence degree of the hypotheses that two belief functions strongly support. These two aspects of dissimilarity are complementary in a certain sense, and their fusion is used as the dissimilarity measure. Then, a new estimation method of weighting factors is presented by using the proposed dissimilarity measure. In the evaluation of weight of a source, both its dissimilarity with other sources and their weighting factors are considered. The weighting factors can be applied in the both importance and reliability discounting rules, but the selection of the adapted discounting rule should depend on the actual application. Simple numerical examples are given to illustrate the interest of the proposed approach.  相似文献   

3.

Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naïve, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.

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4.
The Pythagorean fuzzy set introduced by R. R. Yager in 2014 is a useful tool to model imprecise and ambiguous information appearing in decision and clustering problems. In this study, we present a general type of distance measure for Pythagorean fuzzy numbers (PFNs) and propose a novel ratio index‐based ranking method of PFNs. The novel ranking method of PFNs has more powerful ability to discriminate the magnitude of PFNs than the existing ranking methods for PFNs, which is further extended to compare the magnitude of interval‐valued Pythagorean fuzzy numbers (IVPFNs). The IVPFN is a new extension of PFN, which is parallel to interval‐valued intuitionistic fuzzy number. We introduce a general type of distance measure for IVPFNs. Afterwards, we study a kind of clustering problems in Pythagorean fuzzy environments in which the evaluation values are expressed by PFNs and/or IVPFNs and develop a novel Pythagorean fuzzy agglomerative hierarchical clustering approach. In the proposed clustering method, we define the concept of the dissimilarity degree between two clusters for each criterion and introduce the clustering procedure in the criteria level. To take all the criteria into account, we also introduce the overall clustering procedure, which is based on the overall dissimilarity degrees for a fixed aggregation operator such as the commonly used weighted arithmetic average operator or the ordered weighted averaging operator. In the overall clustering process, (1) we present a deviation degree‐based method to derive the weights of criteria and further obtain the overall clustering results if the weights of criteria are completely unknown; (2) we employ the ratio index‐based ranking method of IVPFNs to obtain the overall clustering results if the weights of criteria are given in advance and are expressed by IVPFNs. The salient feature of the proposed clustering method is that it not only can address the clustering problems in which the weights of criteria are not given precisely in advance but also can manage simultaneously the PFNs and IVPFNs data.  相似文献   

5.
具有层次结构的分类属性在客户细分应用中广泛存在。针对传统相异性度量无法准确反映决策者在与细分目标相关的决策指标上的偏好信息,提出一种改进的距离层次并给出使用该度量,基于聚类分析的客户细分基本流程。该度量利用距离层次计算各分类属性值概念间的相异性,同时引入指标距离的概念描述对于特定指标,决策者在不同分类属性值上的偏好,结合模糊相似优先比决策方法和树的广度优先遍历计算不同分类属性值间的指标距离,最后通过将所求得的概念距离和指标距离进行加权求和以更全面地度量不同分类属性值间的相异性。对陕西省电力公司工业客户进行细分实验的结果表明:与传统距离层次相比,采用改进相异性度量能提高聚类质量和细分结果的可解释性。  相似文献   

6.
In this paper a fuzzy distance measure between two generalized fuzzy numbers is developed. The metric properties of this distance measure are also studied. The new distance measure is compared with the other fuzzy distance measures proposed by Voxman [W. Voxman, Some remarks on distances between fuzzy numbers, Fuzzy Sets and Systems 100 (1998) 353–365] and Chakraborty and Chakraborty [C. Chakraborty, D. Chakraborty, A theoretical development on fuzzy distance measure for fuzzy numbers, Mathematical and Computer Modelling 43 (2006) 254–261] and turned out to be more reasonable. A new similarity measure is also developed with the help of the fuzzy distance measure. Examples are given to compare this similarity measure with the other similarity measure previously proposed. A decision making scheme is proposed using this similarity measure and this scheme is found to be more acceptable than the existing methods due to the fact that it considers the degrees of confidence of the experts’ opinion.  相似文献   

7.
In this paper, in order to improve both the performance and the efficiency of the conventional Gaussian Mixture Models (GMMs), generalized GMMs are firstly introduced by integrating the conventional GMMs and the active curve axis GMMs for fitting non-linear datasets, and then two types of Fuzzy Gaussian Mixture Models (FGMMs) with a faster convergence process are proposed based on the generalized GMMs, inspired from the mechanism of Fuzzy C-means (FCMs) which introduces the degree of fuzziness on the dissimilarity function based on distances. One is named as probability based FGMMs defining the dissimilarity as the multiplicative inverse of probability density function, and the other is distance based FGMMs which define the dissimilarity function focusing the degree of fuzziness only on the distances between points and component centres. Different from FCMs, both of the proposed dissimilarity functions are based on the exponential function of the distance. The FGMMs are compared with the conventional GMMs and the generalized GMMs in terms of the fitting degree and convergence speed. The experimental results show that the proposed FGMMs not only possess the non-linearity to fit datasets with curve manifolds but also have a much faster convergence process saving more than half computational cost than GMMs'.  相似文献   

8.
ABSTRACT

A shape prior-based object segmentation is developed in this paper by using a shape transformation distance to constrain object contour evolution. In the proposed algorithm, the transformation distance measures the dissimilarity between two unaligned shapes by cyclic shift, which is called ‘circulant dissimilarity’. This dissimilarity with respect to transformation of the object shape is represented by circular convolution, which could be efficiently computed by using fast Fourier transform. Given a set of training shapes, the kernel density estimation is adopted to model shape prior. By integrating low-level image feature, high-level shape prior and transformation distance, a variational segmentation model is proposed to solve the transformation invariance of shape prior. Numerical experiments demonstrate that circulant dissimilarity-based shape registration outperforms the iterative optimization on explicit pose parameters, and show promising results and highlight the potential of the method for object registration and segmentation.  相似文献   

9.
The need of suitable measures to find the distance between two probability distributions arises as they play an eminent role in problems based on discrimination and inferences. In this communication, we have introduced one such divergence measure based on well-known Shannon entropy and established its existence. In addition to this, a new dissimilarity measure for intuitionistic fuzzy sets corresponding to proposed divergence measure is also introduced and validated. Some major properties of the proposed dissimilarity measure are also discussed. Further, a new multiple attribute decision-making (MADM) method based on the proposed dissimilarity measure is introduced by using the concept of TOPSIS and is thoroughly explained with the help of an illustrated example on supplier selection problem. Finally, the application of proposed dissimilarity measure is given in pattern recognition and the performance is compared with some existing divergence measures in the literature.  相似文献   

10.
Statistical pattern recognition traditionally relies on feature-based representation. For many applications, such vector representation is not available and we only possess proximity data (distance, dissimilarity, similarity, ranks, etc.). In this paper, we consider a particular point of view on discriminant analysis from dissimilarity data. Our approach is inspired by the Gaussian classifier and we defined decision rules to mimic the behavior of a linear or a quadratic classifier. The number of parameters is limited (two per class). Numerical experiments on artificial and real data show interesting behavior compared to Support Vector Machines and to kNN classifier: (a) lower or equivalent error rate, (b) equivalent CPU time, (c) more robustness with sparse dissimilarity data.  相似文献   

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