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Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network,...
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...
Clustering, a well-known technique, is used to divide a data set into number of groups, called clusters. Differential evolution and particle swarm optimization are robust, fast and very effective search techniques. To increase computational capability, two different quantum inspired meta-heuristics for automatic clustering, have been proposed here. An application of quantum inspired techniques has...
Nowadays with enlargement of power grid and increasing renewable energy integration, uncertainties and randomness challenge traditional power system analysis methods. Scenarios are widely applied to deal with power system uncertainties. Especially power flow examination in transmission expansion planning needs typical scenarios to verify normal power flow, and provide useful information for auxiliary...
The emerging of the intelligent transportation system especially in the research area of traffic surveillance and solving traffic congestions, become notably crucial for traffic operators in the aim of achieving efficient vehicle flow. However, behavioural manoeuvres that describe the pattern of vehicles movements and change of the vehicle flow are not sufficiently modeled based on the conventional...
Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
Modularity is an evaluation measure for graph clustering. Louvain method is constructed by local optimization for modularity and is bottom up method as well as agglomerative hierarchical clustering. Cluster validity measures are used to evaluate cluster partitions as well as modularity. They are traditional evaluation measures in the field of clustering. We propose a novel graph clustering which is...
Clustering ensemble approaches usually have more accurate, robust and stable results than traditional single clustering approaches. However, clustering ensemble can still be improved in the following aspects: (1) improve the diversity of subspaces; (2) employ probabilistic latent clustering; (3) adopt the internal latent factor analysis before the consensus function. Therefore, we propose a new clustering...
P2P media streaming is the most renowned application on the web. In live media streaming nearest neighbor should discover as quick as possible. It is the most time constrained application to find nearest peer over the internet. Scalability, QoS and low latency are the objectives of media streaming. To achieve these objectives a system is needed which speedily and perfectly finds the nearby peer by...
Caching contents at base stations (BSs) has emerged as an effective way to offload backhaul traffic and improve quality of experience (QoE). Considering the limited cache size, how to maximize cache efficiency has become an urgent issue to be addressed. In this paper, we consider caching selected contents at small cell base stations (SBSs) in ultra dense network (UDN). The cache efficiency problem...
A low-complexity algorithm is presented that clusters sensor nodes based on similarity in the sensed signals. This feature makes it an enabler for distributed detection of events that are impossible to identify using information available to a single node. The algorithm does not require system training prior to deployment nor does it assume statistical knowledge of the signal. Experimental results...
Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the...
Traditional machine learning algorithms often require computations on centralized data, but modern datasets are collected and stored in a distributed way. In addition to the cost of moving data to centralized locations, increasing concerns about privacy and security warrant distributed approaches. We propose keybin, a distributed key-based binning clustering algorithm for high-dimensional spaces....
Unsupervised fuzzy clustering is an important tool for finding the meaningful patterns in data sets. In fuzzy clustering analyses, the performances of clustering algorithms are mostly compared using several internal fuzzy validity indices. However, since the well-known fuzzy indices have originally been proposed for working with membership degrees produced by the traditional Fuzzy c-means Clustering...
A solid and practical approach for designing an optimal secondary distribution network is proposed. The methodology starts by optimally locating and sizing medium voltage/low voltage transformers then finding the optimal path for secondary circuits. The optimal number of transformers and their location are determined by k-means clustering algorithm, and validated using Davies-Bouldin index. In addition,...
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...
Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation...
A new color image segmentation of noisy images based on spatial information with the Generalized Dirichlet mixture model is presented. The methodology uses Markov Random Field distribution with a novel factor that is induced in mixture model. The model is learned using Expectation Maximization (EM) algorithm based on Newton-Raphson approach. The obtained results using real images are more encouraging...
This paper discusses a novel methodology for dynamic modeling of writing process. Sequent sub-documents of a given document are described through occurrences of the suitably selected N-grams. The Mean Dependence similarity measures the association between a present sub-document and numerous preceding ones and transforms a document into a time series, which is supposed to be weak stationary if the...
The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to inhomogeneity of characteristics. Furthermore, many algorithms such us FCM, PFCM, FCC, FWCM and modification aim to solve these problems by integrating spacial information. This process...
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