Intuitionistic Fuzzy Clustering Research Articles

INTUITIONISTIC ROBUST CLUSTERING FOR SEGMENTATION OF LESIONS IN DERMATOSCOPIC IMAGES

This paper presents the formulation of the intuitive fuzzy clustering algorithm to be robust to atypical data present in dermoscopic images and to delimit the affected area. This algorithm is formulated from the objective function derivation for memberships update, to integrate an m-redescending estimator influence function. Experimentation shows an accuracy of 95% with the proposal algorithm with respect to other clustering algorithms to perform delimitations, in addition the iterations number is considerably reduced. Keywords: Robust Intuitionistic Fuzzy Clustering, Dermoscopic Images, Delimitations of Lesions, M-redescending Estimator

An electronic medical record access control model based on intuitionistic fuzzy trust

Electronic Medical Records (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and use, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet in the dynamic and complex healthcare environment. We create a model to solve the privacy leakage problem of healthcare big data. The model utilizes the Latent Delicacy Allocation (LDA) topic model to quickly search documents and extract the topic probability distributions between the target patient and other patients' electronic medical records, which improves the efficiency of medical record document analysis; Calculating physician access similarity, we quantify physician access behavior to more accurately describe physician access behavior in the form of data. We combined the intuitionistic fuzzy theory with the trust evaluation method to create a novel trust evaluation method-- Intuitionistic Fuzzy Trust Evaluation Method, which can reflect the uncertainty characteristics that exist in the physician's diagnostic and treatment process when quantifying the trust of the access by using this method, and it is more flexible and practical, which enhances the accuracy of the quantification of the trust. Improving the traditional trust aggregation method by adding a time window and time decay function, the optimized method improves the accuracy of trust aggregation, and the experimental results are more in line with the actual law. We created a new intuitionistic fuzzy trust access control model using a relative entropy-based intuitionistic fuzzy clustering algorithm and a reward and punishment mechanism to achieve adaptive and dynamic access control for physician access behavior. Experiments show that the access control model based on intuitionistic fuzzy trust proposed in this article achieves 95% correctness in identifying user access behaviors and 94.29% correctness in identifying excessive user access behaviors. Experiments have proved that this model has obvious advantages over other models in the process of privacy protection of electronic medical record systems and has certain control effects.

Multi-Source T-S Target Recognition via an Intuitionistic Fuzzy Method

To realize aerial target recognition in a complex environment, we propose a multi-source Takagi–Sugeno (T-S) intuitionistic fuzzy rules method (MTS-IFRM). In the proposed method, to improve the robustness of the training process of the model, the features of the aerial targets are classified as the input results of the corresponding T-S target recognition model. The intuitionistic fuzzy approach and ridge regression method are used in the consequent identification, which constructs a regression model. To train the premise parameter and reduce the influence of data noise, novel intuitionistic fuzzy C-regression clustering based on dynamic optimization is proposed. Moreover, a modified adaptive weight algorithm is presented to obtain the final outputs, which improves the classification accuracy of the corresponding model. Finally, the experimental results show that the proposed method can effectively recognize the typical aerial targets in error-free and error-prone environments, and that its performance is better than other methods proposed for aerial target recognition.

How to promote the sustainable development of strategic emerging industries: Evidence from Guangzhou

The strategic emerging industry (SEI) can effectively cope with high energy consumption, high pollution and low efficiency in traditional production mode, which attracts high attention. However, few studies quantitatively analyze the input-output relationships and the sustainable development level of SEIs. Moreover, existing method frameworks fail to consider both the current situation and development potential of SEIs. To deal with the above research gaps, a novel static-dynamic intuitionistic fuzzy evaluation framework integrating output-oriented data envelopment analysis model, improved Malmquist model, triangular intuitionistic fuzzy number, clustering method and Tobit regression model is first proposed. 120 SEIs from Guangzhou are selected for empirical study. Results reveal that all SEIs have almost no expected movement in operational cost but obvious redundancies in employee expenditure with a standard deviation of 1.771. It's urgent to optimize the employment, assessment and salary systems for staff. Moreover, SEI sustainable development scores vary from 0.473 to 1. It's necessary to strengthen cultivation of excellent SEIs and pay attention to supervision of the weak. Research findings can not only guide local government and industry associations to make scientific evaluation, but also help SEIs carry out self-inspections, which can further promote sustainable development of the entire industry.

Modified total Bregman divergence driven picture fuzzy clustering with local information for brain MRI image segmentation

This research work discusses a noise-robust picture fuzzy clustering method with an application to the MRI image segmentation problem. The MRI images suffer from the problem of noise and non-linear structures. Although there are many variants of fuzzy set theory and intuitionistic fuzzy set theory-based clustering approaches to handle the problem of noise and non-linearity present in the image during the segmentation process, still they lack in achieving accurate segmentation. To overcome this problem, we have suggested using the picture fuzzy set theoretic approach which enhances the representational capability of the data and helps in handling the non-linear structures present in the image, and in our proposed work, the picture fuzzy Euclidean distance is replaced with modified picture fuzzy total Bregman divergence using the spatial neighborhood information around the sample. Squared Euclidean distance is itself a special case of Bregman Divergence, hence Bregman divergence helps to explore the details in a better way thus resulting in noise suppression. Furthermore, the proposed algorithm proves to be robust in preserving the image details using picture fuzzy set theory and is free from any parameter selection due to the incorporation of a local information factor. The algorithm was performed on a synthetic image corrupted with “Gaussian”, “salt and pepper”, “mixture of Gaussian and salt and pepper noise” of different intensities, “Brainweb” datasets corrupted with noise, six real “IBSR” datasets along with seven “MRbrainS18” datasets. Performance measures used were partition coefficient, partition entropy, dice score (DS), average segmentation accuracy (ASA), and XB index, and the proposed method was robust when compared to a series of algorithms stated in the literature.

Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System.

In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini-Mammographic Image Analysis Society (Mini-MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95.

Intuitionistic fuzzy time series forecasting method for non-stationary time series data with suitable number of clusters and different window size for fuzzy rule generation

The strict non-stationary time series (NS-TS) forecasting is one of the challenging tasks as the series does not follow any defined pattern. Previous studies had mainly focused on stationary, seasonal, or trending time series. This study aims to present an effective method for non-stationary time series (NS-TS) forecasting using the intuitionistic fuzzy time series clustering technique. The algorithm is proposed based on the observations and results obtained after the implementation of three existing algorithms with four variants of each. We have used four datasets to test and compare the performance of the proposed model. The experimental results suggest that the method can forecast the NS-TS effectively and more accurately as compared to existing methods.

Density-based IFCM along with its interval valued and probabilistic extensions, and a review of intuitionistic fuzzy clustering methods

Density-based IFCM along with its interval valued and probabilistic extensions, and a review of intuitionistic fuzzy clustering methods

Robust intuitionistic fuzzy clustering with bias field estimation for noisy image segmentation

The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms.

A novel type-II intuitionistic fuzzy clustering algorithm for mammograms segmentation

A novel type-II intuitionistic fuzzy clustering algorithm for mammograms segmentation

A novel attribute reduction method based on intuitionistic fuzzy three-way cognitive clustering

A novel attribute reduction method based on intuitionistic fuzzy three-way cognitive clustering

A bran-new performance evaluation model of coal mill based on GA-IFCM-IDHGF method

Adopting a more scientific and precise assessment method to evaluate the current running performance of coal mills in practice work is essential to maintain the security and reliability of the coal-fired power plant. However, traditional assessment methods have ignored an important problem that the importance among variables varies with the current operation data of coal mill, which has a serious influence on performance evaluation of coal mill. In this paper, a bran-new GA-IFCM-IDHGF assessment method is proposed. Genetic algorithm (GA) is first applied to optimize initial parameters, which is fundamental and significant step to obtain more accurate the number of clusters. Secondly, intuitionistic fuzzy clustering model (IFCM) is adopted to identify the running modes of the coal mill. According to the Delphi, analytic hierarchy process, gray relations analysis and fuzzy integrated evaluation (DHGF), the security range of variables under the normal observed samples from different running modes is acquired, which is helpful to determine whether the current operation data is normal data. Subsequently, an improved Delphi, analytic hierarchy process, gray relations analysis and fuzzy integrated evaluation (IDHGF) method is used to assess the current running performance of coal mill under the abnormal monitoring data. The performance of the proposed performance assessment method is verified through its application in a self-defined step fault system and an actual industrial cases of coal mill. Compared with the traditional methods, the experimental results demonstrate the effectiveness of the proposed method.

CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation

The fuzzy c-means (FCM) algorithm is a popular method for data clustering and image segmentation. However, the main problem of this algorithm is that it is very sensitive to the initialization of primary clusters, so it may not perform well in segmenting complex images. Another problem with the FCM is the equal importance of the image features used during the segmentation process, which causes unstable performance on different images. In this paper, we propose an FCM-based color image segmentation approach, termed CGFFCM, applying an automatic cluster weighting scheme to reduce the sensitivity to the initialization, and a group-local feature weighting strategy to better image segmentation. Also, we combine the proposed clustering algorithm with the Imperialist Competitive Algorithm (ICA) to optimize the feature weighting process. In addition, we apply an efficient combination of image features to increase the segmentation quality. The performance of CGFFCM is evaluated and compared with state-of-the-art methods (such as SMKIFC (semi-supervised surrogate-assisted multi-objective kernel intuitionistic fuzzy clustering), and A-PSO-IT2IFCM (alternate particle swarm optimization based adaptive interval type-2 intuitionistic FCM clustering algorithm)) using the Berkeley benchmark dataset. The results obtained by CGFFCM are 95%, 79%, and 91%, in terms of average Accuracy, NMI, and F-score metrics, respectively, which all are better than the competitors. The implementation source code of CGFFCM is made publicly available at https://github.com/Amin-Golzari-Oskouei/CGFFCM.

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

Spatial information-based intuitionistic fuzzy kernel clustering algorithm for cerebral hemorrhage image segmentation

Customer Behavior Analysis by Intuitionistic Fuzzy Segmentation: Comparison of Two Major Cities in Turkey

The vast quantity of customer data and its ubiquity, as well as the inabilities of conventional segmentation tools, have diverted researchers in search of powerful segmentation techniques for generating managerially meaningful information. Due to its noteworthy practical use, soft computing-based techniques, especially fuzzy clustering, can be considered one of those contemporary approaches. Although there have been various fuzzy-based clustering applications in segmentation, intuitionistic fuzzy sets that have the complimentary feature have appeared in limited studies, especially in a comparative context. Therefore, this study extends the current body of the pertaining literature by providing a comparative assessment of intuitionistic fuzzy clustering. The comparison was carried out with two other well-known segmentation techniques, [Formula: see text]-means and fuzzy [Formula: see text]-means, based on transaction data that belong to Turkey’s two major cities. Over 10,000 records of customers’ data were processed for segmentation purposes, and the comparative approaches were presented. According to the results, the intuitionistic fuzzy clustering approach outperformed the other methods in terms of the clustering efficiency index being utilized. The validity of the segmentation structure obtained by the superior approach was ensured via nonsegmentation variables. The comparative assessment and the potential managerial implications could be considered as a contribution to the corresponding literature. This study also compares the effects of the different parameter values used in the proposed model.

Integrating Structural Symmetry and Local Homoplasy Information in Intuitionistic Fuzzy Clustering for Infrared Pedestrian Segmentation

Interferential background, boundary uncertainty, and noises are usually involved in infrared pedestrian imaging, which erect barrier for accurate segmentation. To counter the conundrum rising in these cases, we present a novel intuitionistic fuzzy clustering-based segmentation method, which integrates structural symmetry and local homoplasy information, for precise infrared pedestrian segmentation. Enlightened by the multiapplication and favorable performance of fuzzy clustering methods, intuitionistic fuzzy c-means (IFCM) is applied as the backbone of our segmentation method. The contributions of the proposed method mainly include two parts. First, motivated by potential target characteristics and tendency for clearer contour description, structural symmetry information is utilized, which is an intrinsic shape feature of objects and would be significant especially when the texture and details of the target are lost in infrared images. Further, a map that represents the probability of pixels belonging to the target is constructed in the form of ellipse symmetry region. Combined with the probability map, symmetry information is utilized to establish a novel dissimilarity function in fuzzy clustering. Second, local homoplasy information which is designed based on region similarity is introduced to suppress the intensity inhomogeneity and noises to further improve the performance of the proposed method. Finally, a dataset containing 500 infrared pedestrian images paired with corresponding pixel-wise annotation is constructed to verify segmentation effectiveness. The proposed SR-IFCM is compared with 12 state-of-the-art segmentation methods. The experimental results indicate that the proposed method outperforms the comparison methods and works better for infrared pedestrian segmentation.

Accelerated intuitionistic fuzzy clustering for image segmentation

Accelerated intuitionistic fuzzy clustering for image segmentation

Superpixels extraction by an Intuitionistic fuzzy clustering algorithm

A scheme to develop the image over-segmentation task is introduced in this paper, it considers the pixels of an image as intuitive fuzzy sets and develops an intuitionistic clustering process of them. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. Experimental tests were developed considering biomedical grayscale and natural color images. The robustness and effectiveness of this proposal was verified by quantitative and qualitative results.

An Intuitionistic Fuzzy Clustering Approach for Detection of Abnormal Regions in Mammogram Images.

Breast cancer is one of the leading causes of mortality in the world and it occurs in high frequency among women that carries away many lives. To detect cancer, extraction or segmentation of lesions/tumors is required. Segmentation process is very crucial if the mammogram images are blurred or low contrast. This paper suggests a novel clustering approach for segmenting lesions/tumors in the mammogram images using Atanassov's intuitionistic fuzzy set theory. The algorithm initially converts an image to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. From the intuitionistic fuzzy image, two membership intervals are computed. Then, using Zadeh's min t-conorm, a new membership function is computed. Using the new membership function, an interval type 2 fuzzy image is constructed. Two types of distance functions are used in clustering-intuitionistic fuzzy divergence and a fuzzy exponential type distance function. Further, in each iteration, membership matrix is updated using a hesitation degree and a clustered image is obtained. Tumors/lesions are then segmented from the clustered image. The proposed method is compared with existing methods both quantitatively and qualitatively and it is observed that the proposed method performs better than the existing methods.

Energy-cost-aware resource-constrained project scheduling for complex product system with activity splitting and recombining

Energy-cost-aware scheduling in manufacturing process has recently been considered as an effective way to use energy. In addition, applying time-of-use is regarded as one of the optimal uses of energy which the governments extend to control energy consumption. Complex product system (CoPS) has more complex and huge amounts of multiple manufacturing activities with higher energy sensibility in project scheduling than commodity products. This paper presents a new variant of resource-constrained project scheduling problem (RCPSP), which is named energy-cost-aware resource-constrained project scheduling problem with activity splitting and recombining (eRCPSP-AS&R) for CoPS. In the proposed model, a dynamic activity splitting and recombining strategy for CoPS is used by considering energy consumption. Also, a bi-objective (project delay and energy consumption cost) mathematical model is established. The solution framework of eRCPSP-AS&R model consists of two parts: activity splitting and recombining, project scheduling. The dynamic strategy of activity splitting and recombining is used to perform energy-cost-aware based on hybrid intuitionistic fuzzy information entropy, TOPSIS and fuzzy clustering method. Moreover, an improved NSGA-II algorithm is employed to solve the Pareto-optimal solutions for the proposed bi-objective optimization problem. Series of computational experiments are conducted and verified the overall performance of proposed eRCPSP-AS&R model and approaches.