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Abstract:Most of the traditional clustering algorithms are poor for clustering more complex structures other than the convex spherical sample space. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications. However, spectral clustering relies on the dataset where each cluster is approximately well separated to a certain extent. In the case that the cluster has an obvious inflection point within a non-convex space, the spectral clustering algorithm would mistakenly recognize one cluster to be different clusters. In this paper, we propose a novel spectral clustering algorithm called HSC combined with hierarchical method, which obviates the disadvantage of the spectral clustering by not using the misleading information of the noisy neighboring data points. The simple clustering procedure is applied to eliminate the misleading information, and thus the HSC algorithm could cluster both convex shaped data and arbitrarily shaped data more efficiently and accurately. The experiments on both synthetic data sets and real data sets show that HSC outperforms other popular clustering algorithms. Furthermore, we observed that HSC can also be used for the estimation of the number of clusters.

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This paper analyses basic concept of elastic cluster as a hybrid solution of high-performance computing tasks for computing grid and cloud. The analysis is focused on the context of managing resources and tasks in the elastic cluster. In this work design, model and implementation of scheduling algorithm is described. The scheduling algorithm is based on particle swarm optimization (PSO) and hill climbing (HC) optimization and it is appropriate combination of good features the both methods. The algorithm is implemented on HPC cluster into the resource manager Torque. There is included methodology of measurement and evaluation of the algorithm. The paper presents methods of verifying behaviour of algorithm for different tasks requirements, which are typical for grid or elastic cluster. We compare suitability of the proposed algorithm with known solutions. On the base of analysed results is confirmed that proposed algorithm better satisfies specific criteria of elastic cluster.