Finding clusters in data is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Herein a new scalable clustering technique which addresses all these issues is proposed. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several years, many clustering algorithms have been proposed in this area of research. Among all these proposed methods, density clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. Moreover these methods often show good noise-handling capabilities, where clusters are defined as regions of typical densities separated by low or no density regions. In this paper, we aim at enhancing the well-known algorithm DBSCAN, to make it scalable and able to discover clusters from uneven datasets in which clusters are regions of homogenous densities. We achieved the scalability of the proposed algorithm by using the k-means algorithm to get initial partition of the dataset, applying the enhanced DBSCAN on each partition, and then using a merging process to get the actual natural number of clusters in the underlying dataset. This means the proposed algorithm consists of three stages. Experimental results using synthetic datasets show that the proposed clustering algorithm is faster and more scalable than the enhanced DBSCAN counterpart
Cloud computing services are becoming ubiquitous, and are becoming the primary source of computing power for both enterprises and personal computing applications. One of the fundamental issues in this environment is related to task scheduling. The scheduler should do the scheduling process efficiently in order to utilize the available resources. In this paper a cloud task scheduling policy based on artificial bee colony algorithm compared with different scheduling algorithms has been proposed. The main goal of the proposed algorithm is minimizing the makespan of a given tasks set. Artificial bee colony algorithm models the behavior of honey bees and can be used to find solutions for difficult or impossible combinatorial problems. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the artificial bee colony algorithm outperformed ACO, FPLTF and FCFS algorithms.
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