Lung cancer classification using fuzzy c-means and fuzzy kernel C-Means based on CT scan image

Fuzzy c-means(FCM)는 퍼지 집합을 응용한 간단하지만 효율적인 클러스터링 방법 중 하나이다. FCM은 여러 응용 분야에서 성공적으로 활용되어 왔지만, 초기화와 잡음에 민감하고 볼록한 형태의 클러스터들만 다룰 수 있는 문제점이 있다. 이 논문에서는 이러한 FCM의 문제점을 해결하기 위해 전역 클러스터링(global clustering) 기법과 커널 클러스터링(kernel clustering) 기법을 결합하여 새로운 비선형 클러스터링 기법인 커널 전역 FCM(kernel global fuzzy c-means, KG-FCM)을 제안한다. 전역 클러스터링은 클러스터링의 초기화를 위한 방법 중 하나로, 순차적으로 클러스터를 하나씩 추가함으로써 초기화에 민감한 FCM의 한계를 극복할 수 있도록 해준다. FCM의 잡음 민감성과 볼록한 클러스터들만 다룰 수 있는 한계를 극복하기 위한 방법은 여러 가지가 있으며 커널 클러스터링이 그 중 하나이다. 커널 클러스터링은 사용하는 커널을 바꿈으로써 쉽게 확장이 가능하므로 이 논문에서는 커널 클러스터링을 사용하였다. 두 방법을 결합함으로써 제안한 방법은 위에서 언급한 문제점들을 해결할 수 있으며, 이는 가상 및 실제 데이터를 이용한 실험 결과를 통해 확인할 수 있다. Fuzzy c-means (FCM) is a simple but efficient clustering algorithm using the concept of a fuzzy set that has been proved to be useful in many areas. There are, however, several well known problems with FCM, such as sensitivity to initialization, sensitivity to outliers, and limitation to convex clusters. In this paper, global fuzzy c-means (G-FCM) and kernel fuzzy c-means (K-FCM) are combined to form a non-linear variant of G-FCM, called kernel global fuzzy c-means (KG-FCM). G-FCM is a variant of FCM that uses an incremental seed selection method and is effective in alleviating sensitivity to initialization. There are several approaches to reduce the influence of noise and accommodate non-convex clusters, and K-FCM is one of them. K-FCM is used in this paper because it can easily be extended with different kernels. By combining G-FCM and K-FCM, KG-FCM can resolve the shortcomings mentioned above. The usefulness of the proposed method is demonstrated by experiments using artificial and real world data sets.

Intrusion Detection System (IDS) has been a challenging area to be developed in recent years since there are so many kinds of network attacks that are difficult to be classified by system. Moreover, there is always the possibility of the upcoming new attack. Therefore, Intrusion detection system must be improved by new classifiers to obtain the best results not only for classifying attack but also recognizing a new type of attack in an advanced way. These developments will be beneficial for managing security systems in an upcoming era. There are several classifier algorithms for Intrusion Detection System such as Support Vector Machine (SVM) and Fuzzy Kernel C-Means (FKCM). SVM classifier is known as one of the techniques to improve efficiency in attack detection while FKCM is famous because it provides better results to reduce the number of selected data features. Each of classifiers is using kernel trick to improve the classifying matter. In this study, we will compare proposed model which are FKCM with rbf, FKCM with polynomial kernel, SVM with rbf, and SVM with polynomial kernel to find a better result that could increase the accuracy of classifying network attacks. KDD Cup 1999 will be used to evaluate each model. In this study, the use of SVM with polynomial kernel brings the best result with 100 % accuracy can be obtained in 1.9 second.

Fuzzy clustering partitions data points of a dataset into clusters in which one data point can belong to more than one cluster. In the literature, a number of fuzzy clustering algorithms have been proposed. This paper reviews various fuzzy clustering algorithms such as Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), Possibilistic Fuzzy C-Means (PFCM), Intuitionistic Fuzzy C-Means (IFCM), Kernel Fuzzy C-Means (KFCM), and Density-Oriented Fuzzy C-Means (DOFCM). We have demonstrated the experimental performance of these algorithms on some standard and synthetic datasets which include—Bensaid, Square (DUNN), D15, and D45 dataset. Then, the results are analyzed and compared to see the effectiveness of these algorithms in presence of noise and outliers.

Aim at the problem of small targets detection in complex infrared background, a method based on the kernel fuzzy clustering and least squares support vector machine (LS-SVM) background predication is proposed. First, partition the training samples by a nearest-neighbor clustering method to get the clustering number and the initial clustering centers. These clustering centers are further processed using kernel fuzzy C-means (KFCM) method. Then, the tuning parameters of the fuzzy model are estimated by LS-SVM. Further these tuning parameters are used to predict the background of infrared images. The prediction image subtracted from the source infrared image gives the residual image. Finally, a threshold selection method based on recursive maximum between-cluster absolute difference is presented to separate the real small target from the residual image. Experimental results are given and they are compared with the results of fuzzy C-means (FCM) detection method. They show that the proposed method has better detection performance.

There is always much difficult in the MR image segmentation. Although fuzzy c-means(FCM) clustering algorithm has been widely used in the field of image segmentation study, some inherent deficiencies of this algorithm especially the high cost of computation made the algorithm to be difficult widely used in practice. A novel algorithm, based on kernel fuzzy c-means (KFCM) clustering algorithm and the k-nearest neighbor (KNN) algorithm, is proposed to improve the performance of MR image segmentation. In this algorithm, the statistical gray level histogram of image is used in KFCM algorithm to speed up the algorithm. Furthermore, the spatial information of image is also considered by k-nearest neighbor algorithm based on kernel methods. With kernel methods each pixel of image is mapped into a high-dimensional feature space where FCM algorithm and KNN algorithm are carried out. Experiments show that the proposed algorithm is effective and efficient in image segmentation.

Image segmentation plays a preliminary and indispensable step in medical image processing. Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm called image Filtering spatial Kernel Fuzzy C-Means(FKFCM) for fuzzy segmentation of magnetic resonance imaging (MRI)data. The algorithm is realized by modifying the objective function in the conventional Fuzzy C-Means (FCM) algorithm using a kernel-induced distance metric, a spatial penalty on the membership functions and then using the image filtering method to correct the image. The algorithm results are compared with standard FCM, Kerneled Fuzzy C-Means (KFCM) and Spatial Fuzzy C-Means(SFCM). The performance of the proposed segmentation algorithm FKFCM provides satisfactory results compared with other algorithms.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

In this paper, we introduce improved segmentation method for segmenting breast MRI images using Kernelized Fuzzy C-Means and Support Vector Machine. Firstly, the new modified Kernelized FCM is constructed in this paper with inclusion of spatial neighborhood term. Then, the data are labeled by new FCM and the input vector for SVM classifier is generated by membership function of proposed new FCM. The robust SVM is proposed for providing effective segmentation result. In both KFCM and SVM, to enlighten the performance of dealing non linearity, the new Quadratic kernel function is used. In order to show the effectiveness of the proposed method, the experimental work is executed on artificially generated random data, and real dynamic contrast enhanced breast MRIs. The superiority of proposed method is proven by comparative analysis of result of proposed and existed methods. To work up the comparative analysis, Fuzzy Partition coefficient, Fuzzy Entropy and Silhouette method are utilized as a cluster validity measure in this paper. Finally, the experimental study shows our proposed method is promising method for segmenting medical images and complex data analysis.

In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing samples are computed by utilizing iterative algorithm, and the clustering result can be acquired according maximum membership principle. Subsequently, the Kernel F cluster validity index is designed for seeking the fitness cluster number and the corresponding relationship model of clusters sequence number and quality grades is constructed. Improving FKCM clustering algorithm is more efficient than other clustering algorithms, and clustering result can provide the training samples for constructing quality grades and clusters recognition function of new samples. The combination of improving FKCM and KF index provides an efficient data analysis method for multi-index dataset.

Fuzzy clustering methods, particularly Fuzzy C-Means (FCM), effectively handle uncertainty in cluster assignments but frequently encounter limitations when dealing with nonlinear and complex data patterns. This preliminary study introduces a novel adaptive distance-enhancement strategy, termed Radial Basis Function-enhanced Fuzzy C-Means (RBF-FCM), which integrates adaptively scaled Gaussian RBF kernels into conventional fuzzy clustering. Comparative experiments on six benchmark datasets, encompassing both nonlinear and structured scenarios, showed that the proposed approach delivered clustering results comparable to standard FCM and Kernelized FCM (KFCM) on simpler datasets, though encountering performance challenges in structured or noisy conditions. Further sensitivity analysis confirmed the critical role of the kernel-width parameter, highlighting its substantial effect on clustering quality. Although these initial experiments did not demonstrate decisive improvements over established methods, they revealed essential directions for future refinement, underscoring the necessity for systematic parameter optimization and adaptive strategies to realize the potential advantages of RBF-enhanced fuzzy clustering.

Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as ‘neutrosophic c-means’ (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM.

Abstract: This work explores a novel approach to accurately identify brain tumors from magnetic resonance imaging (MRI) images by utilizing the complementary capabilities of fuzzy C-Means (FCM) clustering and support vector machines (SVM). Using the soft clustering approach, MRI images are meticulously segregated in the first stage, known as FCM clustering. By giving individual voxels varying degrees of membership, FCM deftly handles the inherent ambiguities and complexity of medical pictures to produce a nuanced representation of tissue kinds. SVM, a potent supervised learning technique for binary classification applications, comes after segmentation. uses this information in the second phase of classification. Through the creation of a hyperplane that divides tumor and non-tumor regions to the greatest extent possible, support vector machines (SVM) are able to optimize the margin between these classes and improve their ability to generalize. We deliberately integrate FCM clustering with SVM classification in order to take use of the complementary characteristics that each of these techniques possesses. While SVM's strong classification skills enable it to fine-tune the identification of tumor regions, FCM's sophisticated segmentation captures the spatial distribution of tissues.

MRI is the most important technique, in detecting the brain tumor. In this paper data mining methods are used for classification of MRI images. A new hybrid technique based on the support vector machine (SVM) and fuzzy c-means for brain tumor classification is proposed. The purposed algorithm is a combination of support vector machine (SVM) and fuzzy c-means, a hybrid technique for prediction of brain tumor. In this algorithm the image is enhanced using enhancement techniques such as contrast improvement, and mid-range stretch. Double thresholding and morphological operations are used for skull striping. Fuzzy c-means (FCM) clustering is used for the segmentation of the image to detect the suspicious region in brain MRI image. Grey level run length matrix (GLRLM) is used for extraction of feature from the brain image, after which SVM technique is applied to classify the brain MRI images, which provide accurate and more effective result for classification of brain MRI images.

Web recommendation systems are helpful in overcoming the excess information on web by retrieving the information required by the user with respect to user's or similar users' preferences and interests. In order to make web recommendation system work, web users have to be clustered based on their common interest. The web user clusters are used to obtain the knowledge about the web pages accessed. This knowledge can be used for prefetching of web pages, finding web pages that are frequently accessed together, etc. This paper presents a modification in the original CA clustering algorithm for grouping of web users with respect to access pattern of web pages. The original CA algorithm uses the basic “Fuzzy C Means (FCM) algorithm” to compute the membership matrix. The modified CA algorithm uses a superior FCM algorithm, namely, the Density Weighted FCM (DWFCM) instead of the basic FCM. Experiments are conducted on the datasets obtained from the UCI repository. It is found that the modified CA clustering method exhibits superior capability of clustering when compared to the CA clustering method.