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This paper proposes a novel fully automatic diagnosis method for liver cirrhosis based on the reading of high-frequency ultrasound images. The proposed method determines the cirrhosis stage via a deep-learning neural network. First, we feed an ultrasound image into an autoencoder to generate the capsule-enhanced version of the image and binarize the enhanced image. Then, we employ a partition-clustering...
Building an ideal graph which reveals the exact intrinsic structure of the data is critical in graph-based clustering. There have been a lot of efforts to construct an affinity matrix satisfying such a need in terms of a similarity measure. A recent approach attracting attention is on using doubly stochastic normalization of the affinity matrix to improve the clustering performance. In this paper,...
Clustering is an effective method for data analysis and can be exploited to unknown features of data samples, its applications range from data mining to bioinformatics analysis. Several clustering approaches have been proposed in order to obtain a better trade-off between accuracy and efficiency of the clustering process. It is well-known that no existing clustering algorithm completely satisfies...
Clustering results are often affected by covariates that are independent of the clusters one would like to discover. Traditionally, Alternative Clustering algorithms can be used to solve such a problem. However, these suffer from at least one of the following problems: i) continuous covariates or non-linearly separable clusters cannot be handled; ii) assumptions are made about the distribution of...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
Brain tumour diagnosis is usually a vital use of medical image processing, where clustering technique commonly used with medical application especially regarding brain tumour diagnosis with magnetic resonance imaging (MRI). In this MRI has been considered because it provides accurate visualization of anatomical structure of tissues. The conventional mean shift technique utilizes radially symmetric...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness...
With wafer fabs running at near full capacity, it is a constant challenge to maintain high yields. Many different products are fabricated by the same equipment. So the sudden change in product yield, a yield excursion, can have a significant impact to many different products. Therefore, it is critical to detect an excursion as early as possible and fix the cause in order to minimize the impact. This...
With the rapid development of society and technology, home service robot is becoming cheaper and smarter. Facing with the difficulties of aging and shortage of labor, we can use home service robot (HSR) as a good companion and servant. However, the security and reliability problems have become bottlenecks in this field. It is meaningful to do researches on fault diagnosis of HSR. Due to its excellent...
We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data. Weighted averaged co-association matrix is calculated using the results of partitioning. We...
In the paper, a rough spatial kernelized fuzzy c-means clustering (RSKFCM) based medical image segmentation algorithm is proposed. This technique is a combination of rough set and spatial kernelized fuzzy c-means clustering (SKFCM). SKFCM is failed to remove the indistinct knowledge that is associated with each data set during the process of its assignment to a particular cluster. The rough set is...
With the evolution of large computer data, every corner of the society is filled with a variety of text information. Indeed, large data information that need manage by people has been unable to meet the rapid development of society. Therefore, the technology of efficient management and accurate positioning of vast quantities of text information has become a hot topic in the research community. In...
Aiming at the multiple attribute decision making problem with three-parameter interval grey numbers, a grey-incidence clustering decision making method based on regret theory is proposed in this paper. First, according to the idea of TOPSIS method, a kind of comprehensive grey interval incidence coefficient of three-parameter interval grey number is defined, and the “regret-rejoice” value is calculated...
Fuzzy clustering has emerged as an important tool for discovering the structure of data. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering. Aimed at the problems of both a local optimum and depending on initialization strongly in the fuzzy c-means clustering algorithm (FCM), a method of kernel-based fuzzy c-means clustering based on fruit fly algorithms...
Data clustering methods have been used extensively for image segmentation in the past decade. In our previous work, we had established that combining the traditional clustering algorithms with a meta-heuristic like Firefly Algorithm improves the stability of the output as well as the speed of convergence. In this paper, we have replaced the Euclidean distance formula with kernels. We have combined...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
With the fast development of various methods for image classification using the bag-of-features model, machines can efficiently classify images by image content. Spatial pyramid matching (SPM) for sparse coding to create the dictionary is a popular and very well performing approach for image classification. The linear SPM was proposed to take advantage of the speed of the linear Support Vector Machine...
In this paper we describe a new framework for creating distributed programmes which are resilient to cluster node failures. Our main goal is to create a simple and reliable model, that ensures continuous execution of parallel programmes without creation of checkpoints, memory dumps and other I/O intensive activities. To achieve this we introduce multi-layered system architecture, each layer of which...
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points using original features, our method seeks the underlying latent representation and...
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