Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Two New Algorithms, Critical Distance Clustering and Gravity Center Clustering


Farag Hamed Kuwil

Karabuk University, Turkey

: J Comput Eng Inf Technol

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 Our new unpublished Gravity Center Clustering (GCC) algorithm falls under partition clustering and is based on gravity center "GC" and it is a point within cluster and verifies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.

Biography


Farag Kuwil is a PhD student in Karabuk University. He has invented two data clustering algorithms. One published in Expert System with Application journal and the second in the first round of review.

Track Your Manuscript

Awards Nomination

GET THE APP