Interval Type-2 Fuzzy C-means Research Articles

Multiview Type-2 kernelized fuzzy C-means clustering with local information for noisy color image segmentation

Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image.

Deep neighborhood structure driven interval type-2 kernel fuzzy c-means clustering with local versus non-local information

Deep neighborhood structure driven interval type-2 kernel fuzzy c-means clustering with local versus non-local information

Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting & clustering weighting

The interval type-2 possibilistic fuzzy C-means (IT2PFCM) algorithm is a popular data clustering and image segmentation method. However, the algorithm fails to consider the spatial information of the image and has limited accuracy for noisy images. The other problem of the IT2PFCM algorithm assumes that all features are equally important in the segmentation process and perform less well as it segments color images. In addition, the IT2PFCM algorithm produces overlapping clusters and even misses some clusters if the groups contained in the data are relatively close to each other. We propose an improved IT2PFCM algorithm for noisy image segmentation to solve the problems. Firstly, an adaptive spatial constraint method is proposed to decrease the algorithm's sensitivity to noise. Secondly, we utilize an automatic local feature weighting and clustering weighting scheme to better segment color images and eliminate overlapping clusters. Finally, an objective function with adaptive spatial constraint, local feature weighting, and clustering weighting is constructed for image segmentation. Extensive experiments on synthetic and natural images show that the proposed algorithm outperforms the state-of-the-art algorithms. The convergence proof of the proposed algorithm is also provided.

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.

Noise-Robust Fuzzy Classifier Designed With the Aid of Type-2 Fuzzy Clustering and Enhanced Learning

This paper introduces the design methodology of a noise-robust fuzzy classifier based on type-2 fuzzy clustering and enhanced learning methods. The design procedure for the noise-robust fuzzy classifier (NrFC) can be divided into two parts. First, interval type-2 fuzzy c-means clustering is applied to the hidden layer to minimize the effect of noise or outliers when training the model. Second, an enhanced learning method is employed to train the connection weights between the hidden and output layers. The proposed NrFC uses a cross-entropy error function as its cost function. The Softmax function represents a categorical distribution located at the output layer nodes. In addition, the connection weights of the output layer are adjusted through nonlinear least squares-based learning, and L2 norm-regularization is considered to avoid the degradation of the generalization ability caused by overfitting. The learning mechanism is realized by adding the L2 penalty term to the cross-entropy error function. It is used to cope with over-fitting and multicollinearity problems, which generally appear in conventional fuzzy neural networks. The design methodology of the NrFC is discussed and analyzed using several publicly available benchmark datasets. The performance of the proposed networks is quantified through comprehensive experiments and comparative analysis.

Robust interval type-2 kernel-based possibilistic fuzzy clustering algorithm incorporating local and non-local information

Type-2 fuzzy set theory has certain potential advantage in processing high-order uncertainty, and has been more and more widely applied in pattern recognition, image processing, and system modeling. Although it has been introduced into robust fuzzy clustering modeling, most clustering methods based on type-2 fuzzy set theory have many obvious shortcomings, such as weak robustness against noise and high sensitivity to initial value. Therefore, this paper presents a novel single fuzzifier interval type-2 kernel-based possibilistic fuzzy local and non-local information c-means clustering, and it is driven by deep neighborhood information for image segmentation in the presence of high noise. Firstly, we construct the novel deep neighborhood window structure, which consists of local neighborhood window around current pixel and deep neighborhood window around the pixel in local neighborhood window around current pixel. Secondly, using the structural similarity between local neighborhood window and deep neighborhood window around current pixel, we construct a novel fuzzy local information factor to tune the impact of local neighborhood information on current pixel clustering. Thirdly, possibility theory and fuzzy local information factor are introduced into interval type-2 kernel-based fuzzy c-means clustering, and a novel single fuzzifier type-2 possibilistic fuzzy clustering with richer local and non-local information and kernel metric is proposed. Experimental results indicate that the proposed algorithm outperforms existing state-of-the-art robust fuzzy clustering-related algorithms, and significantly improve the robustness of robust fuzzy clustering algorithms against noise.

Interval type-2 fuzzy C-means forecasting model for fuzzy time series

The fuzzy time-series (FTS) prediction model is based on fuzzy theory, which handles uncertain and fuzzy data. Owing to its good interpretability and prediction accuracy, the model based on the fuzzy C-means (FCM) clustering algorithm is most widely used. However, the fuzzy parameter m in the FCM algorithm is an empirical value that leads to uncertainty in classification results, which can negatively affect predictions. This study proposes a new model, IT2-FCM-FTS, that uses the interval type-2 (IT2) FCM algorithm instead of the traditional FCM to divide the sample domain and improve the performance of the FTS model. To verify the reliability of the proposed model, five datasets and four evaluation indices are used to compare the prediction results of the proposed model with the traditional ARIMA model and the one based on FCM. The results show that the proposed model is superior to both in terms of prediction accuracy.

A PSO task scheduling and IT2FCM fuzzy data placement strategy for scientific cloud workflows

Scientific workflows are appropriate for treating large volumes of data and leading large-scale experiments. They are time-consuming and resource-intensive applications that profit from operating within distributed platforms, like cloud computing. However, the cloud presents several challenges that must be addressed to employ workflow applications efficiently. Although the workflow scheduling problem has been broadly examined, it still attracts attention. Furthermore, in the literature, little research has been conducted taking into consideration the data placement process in parallel with the execution of task scheduling. Hence, this paper proposes a task scheduling and data placement strategy to reduce the number of data movements between cloud data centers. The suggested strategy uses an algorithm based on the fuzzy clustering method Interval Type-2 Fuzzy C-Means (IT2FCM) and the meta-heuristic optimization technique Particle Swarm Optimization (PSO) to minimize data movements throughout the workflow’s execution. The proposed strategy is evaluated through a simulation process using various well-known scientific workflows of different sizes. Additionally, the outcomes show that our strategy is more reliable than the most recent state-of-the-art techniques.

An improved site characterization method based on interval type-2 fuzzy C-means clustering of CPTu data

An improved site characterization method based on interval type-2 fuzzy C-means clustering of CPTu data

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.

Interval Type-2 Fuzzy C-Means Data Placement Optimization in Scientific Cloud Workflow applications

Scientific workflows stand as practical solutions useful for maintaining data intensive applications representation and execution purposes, which entail not only powerful computing resources, but also massive storage. With the emergence of cloud environment, which enhanced the execution of such applications, the study of workflow placement strategies, as targeted to effectively reduce data movements across data centers, has grown into a highly challenging objective. Given the fact that the workflow execution process is implemented in conformity with a task-execution order, and that each task may deal with either a single or multi-dataset, within a unique data center, various data partitioning or clustering methods have been devised in a bid to retrieve the most optimally effective workflow datasets’ distribution among data centers with the aim of remarkably reducing the datasets’ movements. In this work, a fuzzy data-dependencies based partitioning layer is implemented. More specifically, a dynamic massive data placement strategy is advanced through application of an Interval Type-2 Fuzzy C-Means technique. The latter is opted for as a means whereby the cluster related data centers can be rendered more consistent, thereby, making datasets rather closely associated in terms of related dependency, which helps in remarkably influencing the amounts of transferred data. The proposed strategy is evaluated by means of a simulation technique, using both random and real-world scientific workflows. The performed experiments appear to reveal well that our suggested strategy proves to outperform noticeably the relevant state-of-the-art methods, in that it noticeably helps in reducing the number of data movements across data centers.

P.513 Metabolic syndrome in schizophrenia and polymorphic variants of CYP1A2 and GSTP1 genes

Scientific workflows stand as practical solutions useful for maintaining data intensive applications representation and execution purposes, which entail not only powerful computing resources, but also massive storage. With the emergence of cloud environment, which enhanced the execution of such applications, the study of workflow placement strategies, as targeted to effectively reduce data movements across data centers, has grown into a highly challenging objective. Given the fact that the workflow execution process is implemented in conformity with a task-execution order, and that each task may deal with either a single or multi-dataset, within a unique data center, various data partitioning or clustering methods have been devised in a bid to retrieve the most optimally effective workflow datasets’ distribution among data centers with the aim of remarkably reducing the datasets’ movements. In this work, a fuzzy data-dependencies based partitioning layer is implemented. More specifically, a dynamic massive data placement strategy is advanced through application of an Interval Type-2 Fuzzy C-Means technique. The latter is opted for as a means whereby the cluster related data centers can be rendered more consistent, thereby, making datasets rather closely associated in terms of related dependency, which helps in remarkably influencing the amounts of transferred data. The proposed strategy is evaluated by means of a simulation technique, using both random and real-world scientific workflows. The performed experiments appear to reveal well that our suggested strategy proves to outperform noticeably the relevant state-of-the-art methods, in that it noticeably helps in reducing the number of data movements across data centers.

A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

Although satellite images can provide more information about the earth’s surface in a relatively short time and over a large scale, they are affected by observation conditions and the accuracy of the image acquisition equipment. The objects on the images are often not clear and uncertain, especially at their borders. The type-1 fuzzy set based fuzzy clustering technique allows each data pattern to belong to many different clusters through membership function (MF) values, which can handle data patterns with unclear and uncertain boundaries well. However, this technique is quite sensitive to noise, outliers, and limitations in handling uncertainties. To overcome these disadvantages, we propose a hybrid method encompassing interval type-2 semi-supervised possibilistic fuzzy c-means clustering (IT2SPFCM) and Particle Swarm Optimization (PSO) to form the proposed IT2SPFCM-PSO. We experimented on some satellite images to prove the effectiveness of the proposed method. Experimental results show that the IT2SPFCM-PSO algorithm gives accuracy from 98.8% to 99.39% and is higher than that of other matching algorithms including SFCM, SMKFCM, SIIT2FCM, PFCM, SPFCM-W, SPFCM-SS, and IT2SPFCM. Analysis of the results by indicators PC-I, CE-I, D-I, XB-I, t -I, and MSE also showed that the proposed method gives better results in most experiments.

Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis

Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), since a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs, trained for state detection and health assessment of an industrial gas turbine at various operating conditions. For this purpose, train and test data are generated by applying mechanical fault signatures to gas turbine’s mathematical model. Fuzzy Rule Base is then developed by means of Interval Type-2 Fuzzy C-Means (IT2FCM) clustering, and parameters of the IT2FLSs are optimized using a metaheuristic algorithm. Finally, the performance of the IT2FL based FDI system is compared to several classification techniques. It is concluded that as a compromise among the objectives of online applicability, accuracy, reliability against measurement uncertainty, incipient fault diagnosis, robustness against abrupt sensor failure and generalization capacity, the proposed method demonstrates a promising performance.

Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis

Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability, and large labeled training sets. In this article, we propose a hierarchical fused model based on DL and fuzzy learning to overcome the drawbacks for pixelwise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means and fuzzy neural network. The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while the HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner, and the deterioration of cross-dataset generalization ability is alleviated. In addition, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on the publicly available mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated.

Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method.

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.

Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.

Data Clustering Method Using Efficient Fuzzifier Values Derivation

The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m 1 and m 2 values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m 1 and m 2 are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).

An Interval Type-2 Fuzzy Controller Based on Data-Driven Parameters Extraction for Cement Calciner Process

The stable control of the calciner plant is vital for the clinker quality and energy consumption in the cement calcination process. However, traditional calciner control strategies cannot efficiently deal with complicated characteristics, such as the changes in the material component, process disturbances and uncertainty, and meanwhile cannot obtain knowledge from data. For these challenges, an intelligent control strategy based on an interval type-2 fuzzy logic controller (IT2FLC) is proposed by making full use of process data in this work. The feedback IT2FLC combines with feedforward IT2FLC, taking into account various disturbances. An improved interval type-2 fuzzy C-means (IT2FCM) clustering algorithm is used to extract membership functions and rules, taking into account process uncertainty. Finally, the proposed control strategy is applied to control a calciner simulation process with practical data. The results show that the proposed strategy has better performance than the type-1 fuzzy logic controller (T1FLC) and can better meet the demand for real-life applications.

Modelling the Spectral Uncertainty of Geographic Features in High-Resolution Remote Sensing Images: Semi-Supervising and Weighted Interval Type-2 Fuzzy C-Means Clustering

The spectral uncertainty refers to the diversity and variations of spectral characteristics within a single geographic object or across different objects of the same class. Usually, existing methods represent the spectral characteristics as precise single-valued curves. Thus, the spectral variations cannot be modeled, which further restricts the analysis and classification performance of remote sensing images. On the other hand, unsupervised methods have poor performance in classification and modeling uncertainty, while supervised methods need a large number of samples with high quality. Fuzzy semi-supervised clustering (FSSC) methods achieve a high accuracy with limited labelled samples. Thus, currently, FSSC methods attract more and more attention. This paper proposes a novel method to model the spectral uncertainty for very-high-resolution (VHR) images based on interval type-2 fuzzy sets (IT2 FSs), namely the hierarchical semi-supervising and weighted interval type-2 fuzzy c-means for objects (hierarchical SSW-IT2FCM-O) clustering method. In this method, the VHR image is segmented into image objects to reduce spectral uncertainty within objects. Spectral values, spectral indices and textures were weighted for object-based image classification. To further reduce spectral uncertainty across different objects of the same class, the spectral characteristics of land cover types were represented as banded curves with certain widths instead of precise single-valued spectral curves. The experimental results show that the banded spectral curves produced by the hierarchical SSW-IT2FCM-O can effectively model the spectral uncertainty of geographic objects. From the perspective of classification, four typical validity indices along with the confusion matrix and kappa coefficient were used to test the effectiveness of the hierarchical SSW-IT2FCM-O method, and these indices show that the presented method SSW-IT2FCM-O has greater classification accuracy than the existing FSSC methods and, more importantly, it requires smaller training samples than the existing methods.