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Analyzing social iterations in a scientific environment will assist researchers in expanding their collaborative networks. Scientific social networks represent the researchers' social iterations in an academic environment. The analysis of these networks requires a detailed study of their structure and it is important the use of visual resources in order to a better understanding of how the social...
Text mining discover and extract useful information from documents, whenever increase the size and number documents leads to redouble features. The huge features for the documents adds challenge to text mining called high dimension. The aim of this proposed study is minimize the high dimension of the documents, and improve Arabic text mining using clustering. In order to achieve this goal, we propose...
In order to evaluate and increase modularity this paper combines a method for visualizing and measuring software architectures and two algorithms for decoupling. The combination is tested on a software system at Ericsson. Our analysis show that the system has one large cluster of components (18% of the system, a Core), all interacting with each other. By employing cluster and dominator analysis we...
Image segmentation from noisy CT image has a number of applications in different clinical diagnosis. In order to automatically perform this task from an under-sampled noisy CT image, we have merged compressed sensing reconstruction technique and hierarchical clustering algorithm together in this paper. Denoising based approximate message passing (D-AMP) algorithm is used as a compressed sensing reconstruction...
Similarity search is arguably the most important primitive in time series data mining. Recent research has made significant progress on fast algorithms for time series similarity search under Dynamic Time Warping (DTW) and Uniform Scaling (US) distance measures. However, the current state-ofthe-art algorithms cannot support greater amounts of rescaling in many practical applications. In this paper,...
While accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available...
Packing and placement are two crucial stages for FPGA realization. In the design flow, the basic logic units, such as look-up-tables (LUTs) and flip-flops (FFs), have to be merged into configurable logic blocks (CLBs) before placement. How the basic logic blocks are clustered in the packing stage has a great impact on the placement quality. This work presents an analytical placement framework for...
The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity...
The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval of the formula to be checked. The former issue can be addressed by distributed and parallel...
Superpixel segmentation targets at grouping pixels in an image into atomic regions that align well with the natural object boundaries. In this paper, we propose a novel superpixel segmentation method based on an iterative and adaptive clustering algorithm that embraces color, contour, texture, and spatial features together. The algorithm adjusts the weights of different features automatically in a...
Data mining algorithms are used to analyze and discover useful information from data. This paper presents an experiment that applies Combinatorial Testing (CT) to five data mining algorithms implemented in an open-source data mining software called WEKA. For each algorithm, we first run the algorithm with 51 datasets to study the impact different datasets have on the test coverage. We select one dataset...
This paper presents a new approach of clusteringmechanism in the wireless sensor networks. In our proposedapproach the selection of the cluster head is based on multiplecriteria by combining varieties of performance metrics. Therefore, the main aim of this paper is to enhance the election of themost performed node in order to be a cluster head. The proposedmechanism is evaluated by Matlab and was...
Several research tools and projects require groups of similar code changes asinput. Examples are recommendation and bug finding tools that can providevaluable information to developers based on such data. With the help ofsimilar code changes they can simplify the application of bug fixes and codechanges to multiple locations in a project. But despite their benefit, thepractical value of existing tools...
According to the characteristics of positive and negative edge of signed networks, a new signed networks community detection algorithm BTCN_SNCD (Signed networks Community Detection Based on the Tightness of Common Neighbors) is proposed based on the tightness of common neighbors. Firstly, in view of the shortcomings of the traditional local similarity metrics only considering the number of common...
Improvements to sensor devices including micro-electro mechanical devices that are used for information collection and dissemination has led to the introduction of Wireless Sensor Networks (WSN). Sensor nodes in a WSN are deployed over an area to collect data from the surroundings and to perform additional actions including data aggregation and storage, computations and data transmission to gateway...
This paper is concerned with event clustering for short text streams, which aims to divide constantly arriving short texts into several dynamic event-based clusters. A widely adopted approach is based on the Vector Space Models (VSMs) such as bag of words. However, these models have limitations in that not only the semantic relationships between words are largely ignored, the term weighting may also...
The closest string problem is a core problem in computational biology with applications in other fields like coding theory. Many algorithms exist to solve this problem, but due to its inherent high computational complexity (typically NP-hard), it can only be solved efficiently by restricting the search space to a specific range of parameters. Often, the run-time of these algorithms is exponential...
We propose a new superpixel algorithm based on exploiting the boundary information of an image, as objects in images can generally be described by their boundaries. Our proposed approach initially estimates the boundaries and uses them to place superpixel seeds in the areas in which they are more dense. Afterwards, we minimize an energy function in order to expand the seeds into full superpixels....
Grouping of vehicles into clusters proves to be very rewarding for VANETs as if it reutilizes all the resources within group and thereby increases the system capacity. The main idealization of our work is to provide selection criteria for next available cluster head node (NACH). The various parameters for selection criteria are speed, position, acceleration, directional threshold point, threshold...
Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter λ for the minimum separation. However, without knowing either the true number of clusters or...
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