Multi-dimensional K-Means Algorithm for Student Clustering

Autor:	A. H. Beg, Wan Maseri Binti Wan Mohd, Haruna Chiroma, Ahmad Noraziah, Tutut Herawan
Rok vydání:	2019
Předmět:	Computer science Cluster (physics) Multi dimensional k-means clustering Centroid Data mining computer.software_genre Cluster analysis computer Synthetic data
Zdroj:	Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) ISBN: 9789811317972 DaEng
DOI:	10.1007/978-981-13-1799-6_14
Popis:	K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::948830205892ed634df7cfd545f8e7da https://doi.org/10.1007/978-981-13-1799-6_14 Zobrazit plný text záznamu