Density based Clustering Algorithm for Distributed Datasets using Mutual k-Nearest Neighbors

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 highdensity 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.

A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms

“…The essential technique that has a significant role in document clustering is density-based clustering because it can discover clusters of arbitrary and different shapes [25]. Density-based clustering is a robust algorithm in analyzing specific data with a most remarkable performance [30], and it can provide adequate security in clustering data at various distributed datasets [31][32][33]. Document clustering has been used in many applications like text summarization [23,24,34], key phrase detection [35], and topic modeling [36,37].…”