Neutrosophic Clustering Research Articles

A label-guided weighted semi-supervised neutrosophic clustering algorithm

The traditional neutrosophic clustering method only performs cluster analysis on the data itself, and often ignores the supervision information of data. In order to solve the above problems, a label-guided weighted semi-supervised neutrosophic clustering algorithm is proposed in the paper. On the one hand, the paired constraint information is used to construct the supervision weight coefficient and the distance measurement learning is combined to re-measure the degree of membership of the data and the cluster center; On the other hand, by minimizing the sum of squares of error between membership matrix and label matrix, the purpose of clustering results guided by label information is realized. Experiments on various data sets and comparisons with other clustering algorithms show that the new clustering algorithm can make full use of supervisory information and improve the accuracy of clustering.

Modified cuckoo search algorithm: Feature subset selection &Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

With the recent advances in knowledge, the complication of multimedia has increased expressively and new areas of research have opened up in search of new multimedia content. Content-based image retrieval (CBIR) are used to extract images associated with image queries (IQs) from huge databases. The CBIR schemes accessible at present have limited functionality because they only have a partial number of functions. This document presents an improved cookie detection algorithm with coarse sentences for processing large amounts of data using selected examples. The improved cuckoo detection algorithm mimics the behavior of brood attachment parasites in some cuckoo species, including some birds. Modified cuckoo recognition uses approximate set theory to create a fitness function that takes into account the sum of features and the quality of classification as a small amount. For an image entered as IQ from a database, distance metrics are used to find the appropriate image. This is the central idea of CBIR. The projected CBIR method is labelled and can extract shape features based on the RGB color using the and canny Edge (CED) and neutrosophic clustering algorithm scheme. After YCbCrcolor cut, and the CED to get the features to extract the vascular matrix. The combination of these techniques improves the efficiency of the CBR image recovery infrastructure. In this thesis recursive neural network techniques are used to measure the similarity. In addition, the accuracy of the results is: The recall score is measured to evaluate system performance. The proposed CBIR system provides more precise and accurate values than the complex CBIR system.

Neutrosophic Clustering Algorithm Based on Sparse Regular Term Constraint

Clustering algorithm is one of the important research topics in the field of machine learning. Neutrosophic clustering is the generalization of fuzzy clustering and has been applied to many fields. This paper presents a new neutrosophic clustering algorithm with the help of regularization. Firstly, the regularization term is introduced into the FC-PFS algorithm to generate sparsity, which can reduce the complexity of the algorithm on large data sets. Secondly, we propose a method to simplify the process of determining regularization parameters. Finally, experiments show that the clustering results of this algorithm on artificial data sets and real data sets are mostly better than other clustering algorithms. Our clustering algorithm is effective in most cases.

Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

(T, S)-Based Single-Valued Neutrosophic Number Equivalence Matrix and Clustering Method

Fuzzy clustering is widely used in business, biology, geography, coding for the internet and more. A single-valued neutrosophic set is a generalized fuzzy set, and its clustering algorithm has attracted more and more attention. An equivalence matrix is a common tool in clustering algorithms. At present, there exist no results constructing a single-valued neutrosophic number equivalence matrix using t-norm and t-conorm. First, the concept of a ( T , S ) -based composition matrix is defined in this paper, where ( T , S ) is a dual pair of triangular modules. Then, a ( T , S ) -based single-valued neutrosophic number equivalence matrix is given. A λ -cutting matrix of single-valued neutrosophic number matrix is also introduced. Moreover, their related properties are studied. Finally, an example and comparison experiment are given to illustrate the effectiveness and superiority of our proposed clustering algorithm.

Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. The neutrosophic set, which is an extension of the fuzzy set, has received extensive attention in solving many real-life problems of inaccuracy, incompleteness, inconsistency and uncertainty. In this paper, we propose a new clustering algorithm, the single-valued neutrosophic clustering algorithm, which is inspired by fuzzy c-means, picture fuzzy clustering and the single-valued neutrosophic set. A novel suitable objective function, which is depicted as a constrained minimization problem based on a single-valued neutrosophic set, is built, and the Lagrange multiplier method is used to solve the objective function. We do several experiments with some benchmark datasets, and we also apply the method to image segmentation using the Lena image. The experimental results show that the given algorithm can be considered as a promising tool for data clustering and image processing.

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images.

Modified spatial neutrosophic clustering technique for boundary extraction of tumours in B‐mode BUS images

Breast ultrasound (BUS) images are of poor quality, contain inherent noise and shadow regions. Consequently, the task of tumour segmentation from these images becomes more difficult. In this study, a modified spatial neutrosophic clustering technique has been proposed for automatic boundary extraction of tumours in B-mode BUS images. The contributions of the work are two-fold: (i) spatial information is incorporated in the neutrosophic l -means (NLM) clustering method for better cluster formation and (ii) membership values are updated by using type-2 membership function, which helps in converging the cluster centres to more desirable locations than ordinary fuzzy membership functions. BUS images with manually marked lesions by an experienced radiologist have been used as gold standard/reference images for quantitative comparison. The proposed method has been applied to 60 BUS images and results are recorded in the form of area and boundary error metrics. The performance of the proposed method has been compared with the region growing, fuzzy c-means clustering, watershed segmentation, neutrosophic c-means clustering and NLM clustering methods. From the quantitative and visual results, it has been observed that the proposed method can extract the tumour boundaries more precisely as compared with the other state-of-the-art clustering techniques.

Single-Valued Neutrosophic Clustering Algorithm Based on Tsallis Entropy Maximization

Data clustering is an important field in pattern recognition and machine learning. Fuzzy c-means is considered as a useful tool in data clustering. Neutrosophic set, which is extension of fuzzy set, has received extensive attention in solving many real life problems of uncertainty, inaccuracy, incompleteness, inconsistency and uncertainty.

A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation

In this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm.

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.

Automatic segmentation of tumors in B-Mode breast ultrasound images using information gain based neutrosophic clustering.

Since breast ultrasound images are of low contrast, contain inherent noise and shadowing effect due to its imaging process, segmentation of breast tumors depicting ultrasound image is a challenging task. Thus, a robust breast ultrasound image segmentation technique is inevitable. To develop an automatic lesion segmentation technique for breast ultrasound images. First, the technique automatically detects the suspicious tumor region of interest and discards the unwanted complex background regions. Next, based on the concept of information gain, the technique applies an existing neutrosophic clustering method to the detected region to segment the desired tumor area. The proposed technique computes information gain values from the local neighbourhood of each pixel, which is further used to update the membership values and the cluster centers for the neutrosophic clustering process. Integrating the concept of entropy and neutrosophic logic features into the technique enabled to generate better segmentation results. Results of proposed method were compared both qualitatively and quantitatively with fuzzy c-means, neutrosophic c-means and neutrosophic ℓ-means clustering methods. It was observed that the proposed method outperformed the other three methods and yielded the best Mean (TP: 94.72, FP: 5.85, SI: 93.75, HD: 8.2, AMED: 2.4) and Standard deviation (TP: 3.2, FP: 3.7, SI: 3.8, HD: 2.6, AMED: 1.3) values for different quality metrics on the current set of breast ultrasound images. Study demonstrated that the proposed technique is robust to the shadowing effect and produces more accurate segmentation of the tumor region, which is very similar to that visually segmented by Radiologist.

Automated delineation of thyroid nodules in ultrasound images usingspatial neutrosophic clustering and level set

Automated delineation of thyroid nodules in ultrasound images usingspatial neutrosophic clustering and level set

Texture‐based image segmentation using neutrosophic clustering

This study presents an effective segmentation method which is based on neutrosophic clustering with the integration of texture features for images. The proposed method transforms the image into the neutrosophic domain and then extracts the texture features using analogies of human preattentive texture discrimination mechanisms. Finally, the neutrosophic clustering is employed to segment the images. This method can handle the indeterminacy of pixels to have strong clusters and to perform segmentation effectively with the noisy images. Experiments are performed with various types of natural and medical images to exhibit the performance of proposed segmentation method. The evaluation of proposed method has been done with other segmentation methods to measure its performance which shows its robustness for noisy and textured images.

Single-Valued Neutrosophic Clustering Algorithms Based on Similarity Measures

Clustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) can describe and handle indeterminate and inconsistent information, while fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with it. To cluster the information represented by single-valued neutrosophic data, this paper proposes single-valued neutrosophic clustering algorithms based on similarity measures of SVNSs. Firstly, we introduce a similarity measure between SVNSs based on the min and max operators and propose another new similarity measure between SVNSs. Then, we present clustering algorithms based on the similarity measures of SVNSs for the clustering analysis of single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the single-valued neutrosophic clustering algorithms.

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

Fifty years have gone by since the publication of the first paper on clustering based on fuzzy sets theory. In 1965, L.A. Zadeh had published “Fuzzy Sets” [335]. After only one year, the first effects of this seminal paper began to emerge, with the pioneering paper on clustering by Bellman, Kalaba, Zadeh [33], in which they proposed a prototypal of clustering algorithm based on the fuzzy sets theory. Starting from this paper, several uncertain clustering methods based on different theoretical approaches for modeling the uncertainty have been proposed. The present paper presents a systematic literature review of these clustering approaches. In particular, with respect to the Statistical Reasoning System, we first illustrate the connection between Information and Uncertainty from the perspective of the so-called Informational Paradigm, according to which Information is constituted by “Informational ingredients”, specifically the “Empirical Information,” represented by statistical data, and “Theoretical information” consisting of background knowledge and basic modeling assumptions. We then describe different kinds of uncertainty affecting the Information. Focusing on the uncertainty associated with a particular statistical methodology, i.e. Cluster Analysis, and adopting as theoretical platform the Informational Paradigm, we present a systematic literature review of different uncertainty-based clustering approaches -i.e. Fuzzy clustering, Possibilistic clustering, Shadowed clustering, Rough sets-based clustering, Intuitionistic fuzzy clustering, Evidential clustering, Credibilistic clustering, Type-2 fuzzy clustering, Neutrosophic clustering, Hesitant fuzzy clustering, Interval-based fuzzy clustering, and Picture fuzzy clustering. We thus show how all these clustering approaches are able of managing in different ways the uncertainty associated with the two components of the Informational Paradigm, i.e. the Empirical and Theoretical Information.

Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45±3.5%, false positive rate is 7.32±5.3% and overlap is 93.15±5. 2%, mean absolute distance is 1.8±1.4 pixels, Hausdorff distance is 0.7±0.4 pixels and Dice metric is 94.25±4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.

Neutrosophic Hierarchical Clustering Algoritms

Interval neutrosophic set (INS) is a generalization of interval valued intuitionistic fuzzy set (IVIFS), whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS) is regarded as extension of intuitionistic fuzzy set (IFS). In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distancemeasures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.

Clustering Methods Using Distance-Based Similarity Measures of Single-Valued Neutrosophic Sets

AbstractClustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.