Abstract: 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.

Ahmed Salim and . “Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.3 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100380

@article{Salim2019,
title = {Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100380},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100380},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Ahmed Salim and }
}