DHC: A Distributed Hierarchical Clustering Algorithm for Large Datasets

Author:

Zhang Wei12ORCID,Zhang Gongxuan1,Chen Xiaohui1,Liu Yueqi1,Zhou Xiumin1,Zhou Junlong1

Affiliation:

1. Computer Science and Engineering, Nanjing University of Science and Technology, NO.200 Xiaolingwei Road, Nanjing 210094, P. R. China

2. Computer Science and Technology, Huaiyin Normal University, NO.111 Changjiangxi Road, Huai’an 223300, P. R. China

Abstract

Hierarchical clustering is a classical method to provide a hierarchical representation for the purpose of data analysis. However, in practical applications, it is difficult to deal with massive datasets due to their high computation complexity. To overcome this challenge, this paper presents a novel distributed storage and computation hierarchical clustering algorithm, which has a lower time complexity than the standard hierarchical clustering algorithms. Our proposed approach is suitable for hierarchical clustering on massive datasets, which has the following advantages. First, the algorithm is able to store massive dataset exceeding the main memory space by using distributed storage nodes. Second, the algorithm is able to efficiently process nearest neighbor searching along parallel lines by using distributed computation at each node. Extensive experiments are carried out to validate the effectiveness of the DHC algorithm. Experimental results demonstrate that the algorithm is 10 times faster than the standard hierarchical clustering algorithm, which is an effective and flexible distributed algorithm of hierarchical clustering for massive datasets.

Funder

National Natural Science Foundation of China

Jiangsu Government Scholarship for Overseas Studies

Publisher

World Scientific Pub Co Pte Lt

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture

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