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Ontologies are domain-specific constructs developed for many different purposes. But, they can be different even within the same domain. Ontology matching is a method for finding the same things in between existing ontologies by looking at semantic similarities. Especially, ontology matching algorithms are used for data integration, reuse of existing ontologies, and information discovery. However,...
A distributed network can have faulty links and nodes. In such a case, reaching a decision amongst themselves become extremely difficult. The current work describes an effective solution for reaching an agreement in a distributed system in the presence of such faults. Furthermore, a partitioning method is used to reduce the overhead of exchanging messages.
We study the two-dimensional geometric knapsack problem (2DK) in which we are given a set of n axis-aligned rectangular items, each one with an associated profit, and an axis-aligned square knapsack. The goal is to find a (non-overlapping) packing of a maximum profit subset of items inside the knapsack (without rotating items). The best-known polynomial-time approximation factor for this problem (even...
In view of the shortcomings of the traditional clustering algorithm in intrusion detection system, this paper proposes a method of selecting the initial clustering center based on density, which can overcome the problem of K value in ordinary K-Means. The improved intrusion detection model can achieve good clustering effect. Compared with the traditional K-Means, it is found that the improved algorithm...
Current hierarchical clustering algorithms face the risk of privacy leakage during the clustering process for big dataset. While differential privacy is a relatively recent development in the field of privacy-preserving data mining, offering more robust privacy guarantees. In the paper, BIRCH algorithm under differential privacy is studied and analyzed. Firstly, Diff-BIRCH algorithm which directly...
This paper deals with multiple detection target tracking. We propose a novel algorithm to cover the many-to-one measurements-to-track association where the linear multitarget integrated track splitting algorithm is extended to accommodate for multiple detection target tracking using measurement partition method. After measurement-selection, measurement cells, where one measurement cell may contain...
A major challenge of running applications in clouds is to determine the right number of resources (virtual machines or VMs) to rent in terms of both performance and cost. Such a challenge becomes greater if the application requires to run across multiple resources. In this paper, we address the problem of scheduling scientific workflow applications. The structure of workflows, dictated by precedence/data...
In traditional K-means, the target function only considered the intra-cluster similarities and did not take into account the differences among categories. In order to take into account both the firmness in the same cluster and the dispersion between different clusters, a new objective function based on ideal point is given, and the K-means algorithm based on quasi ideal point method is provided. A...
This paper studies the controllability problem for weighted digraphs with antagonistic interactions. Based on the graph partition theory, the L-equitable partition(L-EP) is proposed. Using L-equitable partition, a graph-theoretic necessary condition is proposed for the controllability of the multi-agent system and an algorithm is given for the computation of the partition. Besides, the upper bound...
Ghost cells are important for distributed memory parallel operations that require neighborhood information, and are required for correctness on the boundaries of local data partitions. Ghost cells are one or more layers of grid cells surrounding the external boundary of the local partition, which are owned by other data partitions. They are used by the local partition when neighbor information is...
This paper tackles the problem of finding the list of solutions with strictly increasing cost for the Semi-Assignment Problem. Four different algorithms are described and compared. The first two algorithms are based on a mathematical model and on a modification of Murty's algorithm, which was designed to find the list of solutions for the classical assignment problem. The third approach is a heuristic...
The proliferation of Internets of Things (IoT) technologies in both industrial and non-industrial settings has led to the accumulation of Big Data sets. Analysis of these high-volume, high-velocity datasets require advanced processing techniques that incorporate parallel and distributed computations. In this paper, we present a novel distributed self-adaptive neural-network algorithm, the Distributed...
Association rule mining is a very essential data mining technique in different fields. The enormous development of the information needs increased computational power. To address this issue, it is important to study executions of mining algorithms. To find out the frequent itemsets is an essential and vital issue in numerous information mining applications. There are many algorithms present to extract...
Community detection in complex networks is very important for decision-making in the real world. Swarm intelligence optimization is an effective method for community detection. However, such algorithms are easy to fall into local optima and tend to ignore smaller communities. This paper proposed a community detection algorithm with locally social spider optimized (LSSO/CD). The network nodes and their...
In this paper, we proposed a fast coding unit (CU) size decision algorithm for High Efficiency Video Coding (HEVC) medical image lossless coding. In detailed, we used the coding information obtained after checking the first two prediction unit (PU) modes inter 2N×2N and Skip to determine whether or not to continue partitioning the current CU. Eight features are extracted from the coding information...
How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs...
Detecting the groundwater runoff connectivity is important for mining and environment protection. However, traditional physical and chemical experiments based approaches are neither efficient nor effective. Experimental results have shown the bacterial community in an isolated well contains unique DNA sequences, and the bacterial communities in connected wells have common DNA sequences that are not...
In semi administered bunching is one of the vital errands and goes for gathering the information objects into classes (groups) to such an extent that the similitude of items inside bunches is high and the comparability of articles between bunches is Less. The dataset once in a while might be in blended nature that is it might comprise of both numeric and unmitigated sort of information. So two types...
Social networks are no longer a place where you can spend leisure time and chat with friends. It is also a business instrument in work with their audiences to increase brand recognition, total result from marketing and move sales up. For this purposes it's needed to make thorough analysis of the target audience, scan dozens of user profiles, reveal their interests, positions and estimate users LTV...
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...
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