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  • 标题:Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors
  • 本地全文:下载
  • 作者:Ahmed Salim
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2019
  • 卷号:10
  • 期号:3
  • 页码:620-630
  • DOI:10.14569/IJACSA.2019.0100380
  • 出版社:Science and Information Society (SAI)
  • 摘要:Privacy and security have always been a concern that prevents the sharing of data and impedes the success of many projects. Distributed knowledge computing, if done correctly, plays a key role in solving such a problem. The main goal is to obtain valid results while ensuring the non-disclosure of data. Density-based clustering is a powerful algorithm in analyzing uncertain data that naturally occur and affect the performance of many applications like location-based services. Nowadays, a huge number of datasets have been introduced for researchers which involve high-dimensional data points with varying densities. Such datasets contain data points with high-density regions surrounded by data points with sparse density. The existing clustering approaches handle these situations inefficiently, especially in the context of distributed data. In this paper, we design a new decomposable density-based clustering algorithm for distributed datasets (DDBC). DDBC utilizes the concept of mutual k-nearest neighbor relationship to cluster distributed datasets with different density. The proposed DDBC algorithm is capable of preserving the privacy and security of data on each site by requiring a minimal number of transmissions to other sites.
  • 关键词:Privacy; mutual k-nearest neighbor; Density-based; clustering; security; DDBC
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