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We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based formulation: we first rigorously show its equivalence with traditional approaches, and then propose QuickMatch,...
In text mining, document clustering describes the efforts to assign unstructured documents to clusters, which in turn usually refer to topics. Clustering is widely used in science for data retrieval and organisation. In this paper we present a new graph theoretical approach to document clustering and its application on a real-world data set. We will show that the well-known graph partition to stable...
Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
Image segmentation is a fundamental problem in image processing and computer vision. Its goal is to separate an image into a collection of distinct regions, after which other high-level tasks can be performed. Nomalized cut (Ncut) algorithm is the most popular one in image segmentation algorithms. However, the number of segmentation regions needs to be specified by users or experts before the Ncut...
Recently, inspired by the human brainstorming process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing...
Wireless Sensor Networks (WSN) consist in a set ofsensor nodes that collect data in the environment and send it to a Base Station that processes the final data. Some challenges may be found, such as minimizing energy consumption and maximize the network lifetime. Many protocols achieve energy savings through network clustering. This paper presents a new modeling graph using flow network to improve...
This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we emphasize the unique...
In this paper, we presented a novel graph-based clustering algorithm (GC). GC contains two main steps: the first step is to create a graph and find out the key nodes as centers, the second step is to divide every data point to each center. The centers are selected from a graph view. Experimental results on 8 datasets demonstrated that GC could do better than k-means, k-medoids, Hierarchical Clustering...
Money Laundering (ML) is the process of cleaning “dirty” money, thereby making the source of funds no longer identifiable. Detecting money laundering activities is a challenging task due to huge volumes of financial transactions being made in a global market on a daily basis. This paper proposes a novel approach for detecting money laundering transactions among large volumes of financial data in an...
Hierarchical clustering has been well-studied in the community of machine learning. Hierarchical clustering algorithms are deterministic, stable, and do not need a pre-determined number of clusters as input. However, they are not scalable for very large data due to their non-linear complexity. In this paper, a new approach is proposed to reduce the complexity of Hierarchical Clustering, improve the...
A system with the property of self-stabilization can have the advantages of fault tolerance, robustness for dynamic topologies, and straightforward initialization. This paper propose a new self-stabilizing algorithm for the proper coloring of edges using a maximum Δ + 1 colors and convergence time O (m (n + Δ)).
In this paper, the problem of head pose estimation is described. The solution consists of several stages. The clustering is a critical step. The clustering of feature points of the image is consuming and important step that needs to simplify and speed up. For this task, it is proposed to use the properties of a random walk on the graph. The random walk can lead to a measure of cluster cohesion. This...
In order to reduce the workload of the airspace controllers and enhance the safety in management, this paper proposed a cycle of bi-partitioned algorithm based on N-cut spectral clustering method and used Dijkstra algorithm with cycled bi-partitioned to divide the airspace sector. What the constraint conditions of minimizing the coordination workload and balancing the monitoring workload were added...
Multi-tenant storage management environments typically manage multiple enterprise accounts with heterogeneous storage footprints. Identifying and grouping accounts with similar storage footprints into clusters reduces account management overhead, and provides a framework for data-driven storage recommendation services. This paper describes a method for the clustering of accounts in multi-tenant storage...
In this paper we present an algorithm for the estimation of geographic locations of Twitter users. The algorithm is based on the graph representation of communication patterns on Twitter and employs an effective clustering algorithm for estimating locations of users from their connections in the communication graph. While using a graph based approach to estimate geo-location for Twitter users is not...
As one of effective emergency control measures that are taken to keep the interconnected power grids from collapse caused by cascading failure, controlled splitting received broad recognition and approbation from engineering field and academic circle. The optimal controlled splitting problem is essentially an optimal graph splitting problem subjected to complicated topology constraints and large-scale,...
Image segmentation is a fundamental process in computer vision applications. This paper presents a novel method to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized...
Several industries are using legacy softwares, developed with Structured Programming (SP) approach, that should be migrated to Object Oriented Paradigm (OOP) for ensuring better software quality parameters like modularity, manageability and extendability. Automating SP to OOP migration is pivotal as it could reduce time that take in the manual process. Given this potential benefit, the issue is yet...
Previous works have analyzed the cluster organization of the cat cortical network using both traditional multidimensional scaling methods and evolutionary optimization algorithms. Interestingly, the evolutionary optimization principle of previous works is based on the modularity measure used to find communities in network with global algorithms. In this paper, we deepen this point taking into account...
After introducing the past research achievements in the field of automatic selection of road network, the advantages and shortcomings of how to select the road network were underlined, then a new road selection algorithm to fetch up the shortcomings was put forward. First, the assessment criteria of the importance of road network were summarized and classified on the basis of constraints in road network...
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