Two New Algorithms, Critical Distance Clustering and Gravity Center Clustering

Farag Hamed Kuwil

Abstract

We developed a new algorithm based on Euclidean distance among data points and employing some mathematical statistics operations and called it critical distance clustering (CDC) algorithm (Kuwil, Shaar, Ercan Topcu, & Murtagh, Expert Syst. Appl., 129 (2019) 296–310. https://authors.elsevier.com/a/1YwCc3PiGTBULo). CDC works without the need of specifying parameters a priori, handles outliers properly and provides thorough indicators for clustering validation. Improving on CDC, we are on the verge of building second generation algorithms that are able to handle larger size objects and dimensions dataset. Our new unpublished Gravity Center Clustering (GCC) algorithm falls under partition clustering and is based on gravity center "GC" and it is a point within cluster and verifies both the connectivity and coherence in determining the affiliation of each point in the dataset and therefore, it can deal with any shape of data, lambda is used to determine the threshold and identify the required similarity inside clusters using Euclidean Distance. Moreover, two coefficients lambda and n provide to the observer some flexibility to control over the results dynamically (parameters and coefficients are different, so, in this study, we assume that existing parameters to implement an algorithm as disadvantage or challenge, but existing coefficient to get better results as advantage), where n represents the minimum number of points in each cluster and lambda is utilized to increase or decrease number of clusters. Thus, lambda and n are changed from the default value in case of addressing some challenges such as outliers or overlapping.