Interval Type-2 Fuzzy Clustering Research Articles

Interval type-2 possibilistic picture C-means clustering incorporating local information for noisy image segmentation

Picture fuzzy C-means clustering is a novel computational intelligence method that has some advantages over fuzzy clustering in pattern analysis and machine intelligence. However, picture fuzzy clustering is easily affected by noise and weighting exponent, which seriously limits its widespread application. To address this issue, this paper proposes a new robust possibilistic clustering method called “interval type-2 possibilistic picture C-means clustering with local information”. This method combines interval type-2 fuzzy sets with possibilistic C-means clustering based on picture fuzzy sets, strengthening the noise resistance of picture fuzzy clustering. Firstly, this paper creatively extends an improved possibilistic clustering with double weighing exponents to picture fuzzy sets, solving the problem of consistency clustering in existing possibilistic picture clustering. Second, this paper originally introduces a new picture local information factor in possibilistic picture clustering and further enhances the anti-noise robustness of the method by using spatial possibilistic picture partition information. Finally, this paper skillfully extends this clustering method to interval type-2 fuzzy sets, which can handle more flexibly high-order uncertainties than type-1 clustering method. Experimental results indicate that this proposed method has better segmentation performance and stronger noise suppression ability compared with existing picture fuzzy clustering and interval type-2 fuzzy clustering. In summary, this work has made significant contributions to the development of picture fuzzy clustering theory and its applications.

Adaptive enhanced interval type-2 possibilistic fuzzy local information clustering with dual-distance for land cover classification

Land cover classification of remote sensing image is faced with uncertainties such as “significant difference in category density”, “the same object with different spectra”, and “different objects with the same spectrum”. Existing possibilistic fuzzy clustering-related method still cannot fully meet the interpretation requirements of remote sensing data. To accurately classify remote sensing image with complex geographical distribution, this paper proposes a robust interval type-2 dual-distance driven possibilistic fuzzy clustering motivated by interval-valued number for land cover classification. Firstly, interval-valued data model is established by using the local mean and variance of single-valued data. Secondly, Hausdorff distance and MW distance for interval-valued numbers are introduced to realize the maximum separability of overlapping categories in interval-valued data and generate interval uncertainty sets. To further enhance its robustness, weighted local information factors are constructed by making full use of the generated fuzzy membership and possibilistic typicality, and a novel robust interval type-2 possibilistic fuzzy C-means clustering is proposed. Finally, the adaptive type reduction method is introduced, meanwhile the adaptive expansion of interval-valued data model is realized by using the adaptive contraction expansion control factor. After the iterative algorithm convergences, the accurate classification of covered objects in remote sensing images is realized. Experimental results indicate that the classification performance of the proposed algorithm is better than that of existing interval type-2 fuzzy clustering algorithm and its variants, and it is more suitable for the interpretation of remote sensing images in the actual environments.

An Adaptive Self-Reduction Type-2 Fuzzy Clustering Algorithm for Pattern Recognition

Decisions in real-life can be adversely affected by various uncertainty-sources such as perception-diversity, data-structure and analytical tools. Fuzzy clustering can successfully handle the uncertainties while recognizing patterns in any given data. Nevertheless, type-1 fuzzy clustering techniques has uncertainties on account of precise-nature of primary memberships. Type-2 fuzzy clustering are preferred by many researchers to manage uncertainty in its type-1 version. In type-2 fuzzy clustering, order of fuzziness (fuzzifier) is obtained by interval-valued or general type-2 fuzzy sets. Interval type-2 fuzzy clustering can reduce the computational complexity of type-2 fuzzy set mathematics. However, general type-2 fuzzy clustering scrutinizes uncertainty in fuzzifier using linguistic sets. Interval and general type-2 fuzzy clustering algorithms include type-reduction approaches to obtain type-1 fuzzy sets. Besides, full type-2 fuzzy c-means can be used as a foundation approach in type-2 fuzzy inferences. Although this algorithm includes precise-fuzzifier, it gives a point of view to practically calculate secondary memberships. In this paper, an adaptive type-2 fuzzy clustering algorithm is proposed to manage the uncertainty-sources with a self-reduction procedure. Several numerical results and comparisons are given to demonstrate the achievement of this proposed algorithm. The performance of the proposed algorithm is compared with type-1 and type-2 versions for various multi-dimensional pattern sets from UCI-patterns, Berkeley segmentation database and a real-life application related to sustainable supplier selection in an automotive industry. Consequently, the proposed algorithm reveals fast, convenient and precise results.

Autoregressive-Elephant Herding Optimization based Generative Adversarial Network for copy-move forgery detection with Interval type-2 fuzzy clustering

A copy move forgery is considered as the general form of digital image forgery, where a portion of the image is copied as well as pasted in some other position in the same image. To act upon forgery is simple; however detecting the forgery is more complex due to the copied portions’ features similar to other parts of images. Therefore, an effectual forgery object detection approach is exploited by exploiting the adopted Autoregressive Elephant Herding Optimization based Generative Adversarial Network (A-EHO based GAN). The proposed A-EHO approach is derived by incorporating Conditional Autoregressive Value at Risk by Regression Quantiles (CAViaR) with Elephant Herding Optimization (EHO). First, features like Local optimal oriented pattern (LOOP) and Convolutional Neural Network (CNN) features are extracted for each foreground object. Then, the features are employed to the GAN for the computation of the forgery score. Here, the RideNN classifier detects the forgery image based on the feature vector and the forgery score. As a result, the adopted approach achieved higher performance regarding detection rate, ROC, TNR, as well as TPR with the values of 96.687%, 98.35%, 97.809%, and 96.971%, respectively.

Remote sensing image change detection based on improved interval type-2 fuzzy clustering

Remote sensing image change detection based on improved interval type-2 fuzzy clustering

A large-scale group decision-making method for site selection of waste to energy project under interval type-2 fuzzy environment

The waste to energy (WTE) project provides a promising solution for managing the rapidly generated municipal solid waste (MSW). To achieve the sustainable MSW management, it is essential to select an appropriate WTE plant site. Thus, this paper proposes a large-scale group decision-making method to select the optimal site. First, a sustainable evaluation index system is constructed based on the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis from the perspectives of economic, environment, and society. Second, considering the linguistic terms provided by the decision makers cannot be directly computed, an interval data driven-based computing with word model is presented to transform the linguistic terms into the Gaussian interval type-2 fuzzy sets (IT2FSs). Then, a Gaussian interval type-2 fuzzy clustering approach is provided to conquer the inefficiency caused by the increase of the number of decision makers. Finally, the TODIM method is extended to compare and rank the alternative sites in the context of Gaussian IT2FSs. A case study is provided to illustrate the effectiveness of the proposed method, meanwhile, some discussions are conducted to illustrate the superiority and flexibility of the proposed method. The results indicate that the proposed method can successfully select the optimal plant site and has the potential to provide practical reference for the policymakers.

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology consist of an interval type-2 fuzzy clustering algorithm based on adaptive similarity distance mechanism for defining specific operation regions associated to the behavior and uncertainty inherited to epidemiological data, and an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm for adaptive tracking and real time forecasting according to unobservable components computed by recursive spectral decomposition of experimental epidemiological data. Experimental results and comparative analysis illustrate the efficiency and applicability of proposed methodology for adaptive tracking and real time forecasting the dynamic propagation behavior of novel coronavirus 2019 (COVID-19) outbreak in Brazil.

A Novel Single Fuzzifier Interval Type-2 Fuzzy C-Means Clustering With Local Information for Land-Cover Segmentation

In the process of land cover segmentation from remote sensing image, there are some uncertainties such as “significant difference in class density”, “different objects with same spectrum”, and “same object with different spectra”. Existing fuzzy c-means clustering is not sufficient to describe the high-order fuzzy uncertainties and cannot achieve accurate segmentation. Type-2 fuzzy set is perfect for handling with interclass multiple uncertainties, and clustering algorithm can suppress the noise of remote sensing image effectively by incorporating local information. Therefore, on the basis of integrating local information, this article proposes a robust single fuzzifier interval type-2 fuzzy local C-means clustering based on adaptive interval-valued data for land cover segmentation. First, interval-valued data modeling is performed for remote sensing data, and remote sensing features are represented as interval-valued vectors, and the robust interval-valued distance measure that can maximize the distance between interval-valued numbers is used to generate an interval type-2 fuzzy set through robust fuzzy clustering. Second, this article adopts an efficient type reduction method to seek equivalent type-1 fuzzy set adaptively, and realizes the segmentation of land cover by the principle of maximum type-1 fuzzy membership. The test results of multispectral remote sensing images show that the segmentation performance of this proposed algorithm outperforms existing state of the art adaptive interval type-2 fuzzy clustering algorithms, and it is beneficial to the interpretation of remote sensing image.

An interval Type-2 fuzzy sets generation method for remote sensing imagery classification

In this paper, we focus on the uncertainty in land cover classification that is caused by the heterogeneity of similar ground objects and the complex spatial correlation of remote sensing image pixels. We propose a method for generating interval type-2 fuzzy sets that incorporates neighborhood information to improve the classification accuracy. First, a correlation model between neighboring pixels is established, and it is used to filter remote sensing images. Then, the interval type-2 fuzzy sets are generated by using the original image pixels and the filtered pixels. Finally, an interval type-2 fuzzy clustering method based on neighborhood information is proposed. To demonstrate the advantages and effectiveness of the proposed method, we ran experiments utilizing two remote sensing images with complex land cover. The results indicate that the proposed method considers both the color information in a sample and its neighborhood relationship. The proposed method can effectively suppress the heterogeneity of the same objects in remote sensing images and can improve the classification accuracy.

Alternate PSO-Based Adaptive Interval Type-2 Intuitionistic Fuzzy C-Means Clustering Algorithm for Color Image Segmentation

Interval type-2 fuzzy c-means (IT2FCM) clustering algorithm can describe more uncertainty than fuzzy c-means (FCM) clustering algorithm by using two fuzzifiers to construct a more inclusive boundary. How to obtain appropriate fuzzifiers and initialize cluster centers are essential tasks for the IT2FCM. To effectively solve these problems, this paper proposes an alternate particle swarm optimization-based adaptive interval type-2 intuitionistic fuzzy c-means clustering algorithm (A-PSO-IT2IFCM) and applies this proposed method to color image segmentation. First, in order to further deal with the uncertainty, a novel interval type-2 fuzzy clustering objective function is constructed by utilizing the intuitionistic fuzzy information extracted from images. Then an alternate particle swarm optimization (PSO) scheme is designed to optimize fuzzifiers and cluster centers alternatively. In addition, a multiscale update strategy for the positions of particles is introduced into the A-PSO-IT2IFCM to increase the diversity of swarm and boost the convergence of optimization. The color image segmentation experiments on Berkeley and UC Merced Land Use datasets show that the proposed algorithm can adaptively determine fuzzifiers and cluster centers and achieve good segmentation results.

Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis

It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to object-based interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an interval-valued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms.

불확실성을 고려한 퍼지 클러스터링 기반 퍼지뉴럴네트워크 설계

산업이 발달함에 따라서 빅데이터가 무수히 생산되고 있으며 이에 따라서 데이터에 내재된 불확실성도 증가하고 있다. 본 논문에서는 데이터에 내재된 불확실성을 다루기 위해 interval type-2 퍼지 클러스터링 방법을 제안하고 이를 이용하여 퍼지뉴럴네트워크를 설계하고 최적화한다. 제안한 클러스터링 방법을 이용하여 퍼지 규칙을 설계하고 학습을 수행한다. 최적화하는 방법으로서 유전자 알고리즘을 이용하고 모델 파라미터들을 최적 탐색한다. 실험에서는 두 가지 패턴 분류를 시행하였으며 두 가지 실험 모두 우수한 패턴 인식 결과를 보여준다. 제안한 네트워크는 증가하는 불확실성을 다룰 수 있는 방법을 제공할 수 있을 것이다. As the industries have developed, a myriad of big data have been produced and the inherent uncertainty in the data has also increased accordingly. In this paper, we propose an interval type-2 fuzzy clustering method to deal with the inherent uncertainty in the data and, using this method, design and optimize the fuzzy neural network. Fuzzy rules using the proposed clustering method are designed and carried out the learning process. Genetic algorithms are used as an optimization method and the model parameters are optimally explored. Experiments were performed with two pattern classification, both of the experiments show the superior pattern recognition results. The proposed network will be able to provide a way to deal with the uncertainty increasing.

Semi-supervising Interval Type-2 Fuzzy C-Means clustering with spatial information for multi-spectral satellite image classification and change detection

Data clustering has been widely applied to numerous real-world problems such as natural resource management, urban planning, and satellite image analysis. Especially, fuzzy clustering with its ability of handling uncertainty has been developed for image segmentation or image analysis e.g. in health image analysis, satellite image classification. Normally, image segmentation algorithms like fuzzy clustering use spatial information along with the color information to improve the cluster quality. This paper introduces an approach, which exploits local spatial information between the pixel and its neighbors to compute the membership degree by using an interval type-2 fuzzy clustering algorithm, called IIT2-FCM. Besides, a Semi-supervising Interval Type-2 Fuzzy C-Means algorithm using spatial information, called SIIT2-FCM, is proposed to move the prototype of clusters to the expected centroids which are pre-defined on a basis of available samples. The proposed algorithms are applied to the problems of satellite image analysis consisting of land cover classification and change detection. Experimental results are reported for various datasets of the LandSat7 imagery at multi-temporal points and compared with the results produced by some existing algorithms and obtained from some survey data. The clustering results assessed with regard to some validity indexes demonstrate that the proposed algorithms form clusters of better quality and higher accuracy in problems of land cover classification and change detection.

Interval type-2 credibilistic clustering for pattern recognition

This paper presents a new approach to interval type-2 fuzzy clustering. In order to consider compactness within the clusters and separation of them simultaneously, the objective function of this paper is designed such that it generates both degrees of membership and non-membership of each data in each cluster, and integrates them using credibility concept. Also, a new approach to separation of clusters is proposed and utilized in designing the objective function. In this approach, the borders of clusters and therefore their compactness contribute in attaining their separation. So, the separation of clusters is not assessed only by the distance of their centers. The credibility degrees are transformed to interval type-2 form to handle different sources of uncertainty. Finally, a new validity index to characterize the number of clusters based on proposed approach to separation of clusters and Choquet integrals are proposed. The advantages of paper contributions are illustrated using several examples.

Enhancing clustering quality of geo-demographic analysis using context fuzzy clustering type-2 and particle swarm optimization

Geo-Demographic Analysis, which is one of the most interesting inter-disciplinary research topics between Geographic Information Systems and Data Mining, plays a very important role in policies decision, population migration and services distribution. Among some soft computing methods used for this problem, clustering is the most popular one because it has many advantages in comparison with the rests such as the fast processing time, the quality of results and the used memory space. Nonetheless, the state-of-the-art clustering algorithm namely FGWC has low clustering quality since it was constructed on the basis of traditional fuzzy sets. In this paper, we will present a novel interval type-2 fuzzy clustering algorithm deployed in an extension of the traditional fuzzy sets namely Interval Type-2 Fuzzy Sets to enhance the clustering quality of FGWC. Some additional techniques such as the interval context variable, Particle Swarm Optimization and the parallel computing are attached to speed up the algorithm. The experimental evaluation through various case studies shows that the proposed method obtains better clustering quality than some best-known ones.

A New Interval Type-2 Fuzzy Clustering Algorithm for Interval Type-2 Fuzzy Modelling with Application to Heat Treatment of Steel

Abstract In this paper, a new hierarchical data-driven modelling strategy based on Interval Type-2 Fuzzy Clustering is elicited for the Interval Type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System. This framework which we have called the IT2-Squared framework uses interval type-2 fuzzy clustering for initial antecedent parameters and structures determination and least-squares algorithm for deriving initial consequent parameters. To improve the accuracy of the system, we show how the steepest descent algorithm is used to tune the parameters of both the consequent and antecedent parameters. To test the efficacy of this proposed system, the model is used on a real-life engineering project for the prediction of the ultimate tensile strength (UTS) of steel. Results show excellent generalization properties of the IT2-Squared modelling framework when compared to previously elicited models of the same system.

A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation

The fuzzy C-means (FCM) algorithm has significant importance compared to other methods in Medical image segmentation. Conventional FCM algorithm is sensitive to noise especially in the presence of intensity inhomogeneity in MRI. Main reason is that a single fuzzifier in FCM cannot properly represent pattern memberships for all clusters. In this paper, we present a novel algorithm for fuzzy segmentation of MRI data. The algorithm utilizes two fuzzifiers used in interval type-2 FCM and a spatial constraint on the membership functions. Also, in our investigation, validity functions are extended to generalized form for interval type-2 fuzzy clustering. The experimental results on both synthetic and MR images show that the proposed algorithm has better performance on image segmentation than conventional FCM based algorithms.

Reinforcement Ant Optimized Fuzzy Controller for Mobile-Robot Wall-Following Control

This paper proposes a reinforcement ant optimized fuzzy controller (FC) design method, called RAOFC, and applies it to wheeled-mobile-robot wall-following control under reinforcement learning environments. The inputs to the designed FC are range-finding sonar sensors, and the controller output is a robot steering angle. The antecedent part in each fuzzy rule uses interval type-2 fuzzy sets in order to increase FC robustness. No a priori assignment of fuzzy rules is necessary in RAOFC. An online aligned interval type-2 fuzzy clustering (AIT2FC) method is proposed to generate rules automatically. The AIT2FC not only flexibly partitions the input space but also reduces the number of fuzzy sets in each input dimension, which improves controller interpretability. The consequent part of each fuzzy rule is designed using Q-value aided ant colony optimization (QACO). The QACO approach selects the consequent part from a set of candidate actions according to ant pheromone trails and Q-values, both of whose values are updated using reinforcement signals. Simulations and experiments on mobile-robot wall-following control show the effectiveness and efficiency of the proposed RAOFC.