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	MR-DBSCAN: An efficient parallel density-based clustering algorithm using MapReduce
	Yaobin He; Haoyu Tan; Wuman Luo; Huajian Mao; Di Ma; Shengzhong Feng; Jianping Fan
	2011
会议名称	2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011
会议地点	Tainan, Taiwan
英文摘要	Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/3588]
专题	深圳先进技术研究院_数字所
作者单位	2011
推荐引用方式 GB/T 7714	Yaobin He,Haoyu Tan,Wuman Luo,et al. MR-DBSCAN: An efficient parallel density-based clustering algorithm using MapReduce[C]. 见:2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011. Tainan, Taiwan.

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