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Health care data collections are usually characterized by an inherent sparseness due to a large cardinality of patient records and a variety of medical treatments usually adopted for a given pathology. Innovative data analytics approaches are needed to effectively extract interesting knowledge from these large collections. This paper presents an explorative data mining approach, based on a density-based...
We present a solution to a specific version of one of the most fundamental computer science problem - the nearest neighbour problem (NN). The new, proposed variant of the NN problem is the multispace, dynamic, fixed-radius, all nearest neighbours problem, where the NN data structure handles queries that concern different subsets of input dimensions. In other words, solutions to this problem allow...
Sophisticated consumers and highly competitive environment has forced designers and manufacturers to realize the importance of consumer emotion in creative product design. Kansei Engineering (KE) is a technology that enables discovery of consumer's emotions when interacting with a product and use them to formulate a guide in designing products that can win consumers in a competitive market. However,...
Recent advances in analysis of fMRI have established the existence of functional sub-networks in the human brain that are active during the performance of visual, motor, language, and other tasks. We describe two computational methods of delineating functional sub-networks that are active when an individual performs an approach-avoidance paradigm. The paradigm consisted of presentation of images of...
In this paper, the problem of content-aware user clustering and content caching in wireless small cell networks is studied. In particular, a service delay minimization problem is formulated, aiming at optimally caching contents at the small cell base stations (SCBSs). To solve the optimization problem, we decouple it into two interrelated subproblems. First, a clustering algorithm is proposed grouping...
In this tool demonstration paper, we present CLUFFP (Clustering For Fault Prediction) an Eclipse plug-in to group source code classes. The clustering approach implemented in our Eclipse plug-in has been successfully applied in the context of fault prediction of object oriented software systems.
This paper presents a method to fill in missing data in an image. If the missing data are clustered in forms of an empty shape, then a similarity pattern searching and filling is performed. The missing data areas are divided into a set of windows of equal size. Each windowed area will be compared with every other non-missing data area of the original image to find the area that is most similar to...
As a product of Web2.0, micro-blog is developing rapidly these years. More and more information spread on the micro-blog because of its high speed and convenience, social hotspots and news events included. As a result, discovering, extraction and analyzing information become researching hotspots. By studying micro-blog text and long text cluster, this article draws a conclusion that traditional cluster...
This paper presents a clustering and optimizing pixel prediction method for reversible data hiding, which exploits self-similarities and group structural information of image patches. Pixel predictors plays an important role for current prediction-error expansion (PEE) based reversible data hiding schemes. Instead of using a fixed or a content-adaptive predictor for each pixel independently, we first...
The use of word senses in place of surface word forms has been shown to improve performance on many computational tasks, including intelligent web search. In this paper we propose a novel approach to automatic discovery of word senses from raw text, a task referred to as Word Sense Induction (WSI). Almost all the WSI approaches proposed in the literature dealt with monolingual data and only very few...
Text documents are often high dimensional and sparse, it is a great challenge to discover the clusters among the unlabelled text data, because there are no obvious clusters by common distance measure. In this paper we present a latent subspace clustering method to find text clusters. In our algorithm, we use latent factors extracted by probability latent semantic analysis (PLSA) to generate latent...
Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail...
Multi-dimensional classification assigns an unseen instance to more than one class variable simultaneously. Bayesian chain classifiers have been recently proposed to address the task since they were first proposed in 2011. However, when the simplistic structure of Bayesian network is built to represent the dependency relationships among classes, the predictive performance will degrade somewhat. In...
Radial basis function (RBF) neural network is widely used in engineering with its powerful advantage in solving nonlinear problems. But the number of hidden layer as well as the center and standard deviation of radial basis function are difficult to get, so RBF neural network based on ENN2 is proposed to solve the fault diagnosis problem. Firstly, the structure of RBF neural network is introduced,...
New e-services come on-line each year at an exponential rate. Most of them have the need to analyze and interpret enormous quantities of data. However, many of them do not take into account the emotions and sentiments in the Web page for their analysis. Thus, in this work, we proposed a novel system to obtain data of interest from a Web search engine by analyzing the emotional and sentimental content...
In this paper, we examine distributed and fair resource allocation for weighted sum multicell capacity maximization in multi-input single-output (MISO)-Orthogonal Frequency-Division Multiple Access (OFDMA) multicell systems with reduced feedback links. We apply zero forcing (ZF) precoding to reduce interference between the users in the same cell and then propose iterative distributed power allocation...
The rapid development of online social networks (OSN) renders them a powerful tool for information diffusion. Understanding the temporal behavior of OSN users is critical in studying the diffusion process. While there is much work on building various diffusion models to characterize the information propagation process, the diversity of OSN users' behavior patterns is seldom addressed in these models...
Online social networks have become popular for communication among people. Previous works on social networks were focused mainly on a single network due to the lack of links from one network to the others. There are several ways to connect different social networks. One possible solution is to find out the same individual who owns accounts for different social networks simultaneously. In this paper,...
Hybrid algorithms incorporated with parallel processing techniques are very powerful tools for efficiently solving very complex optimization problems. We present asynchronous parallel computer architecture adaptation based on hybridization of Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). In this master-slave formulation, slaves perform evolutionary computation independently...
Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, increasing number of web services has been developed. However, this abundance creates a vast collection of web services which makes the task of locating a suitable one more challenging and more difficult. Automatic clustering of web services...
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