Author:
Nainggolan Rena,Perangin-angin Resianta,Simarmata Emma,Tarigan Astuti Feriani
Abstract
Abstract
K-Means is a simple clustering algorithm that has the ability to throw large amounts of data, partition datasets into several clusters k. The algorithm is quite easy to implement and run, relatively fast and efficient. Another division of K-Means still has several weaknesses, namely in determining the number of clusters, determining the cluster center. The results of the cluster formed from the K-means method is very dependent on the initiation of the initial cluster center value provided. This causes the results of the cluster to be a solution that is locally optimal. This research was conducted to overcome the weaknesses in the K-Means algorithm, namely: improvements to the K-Means algorithm produce better clusters, namely the application of Sum Of Squared Error (SSE) to help K-Means Clustering in determining the optimum number of clusters, From this modification process, it is expected that the cluster center obtained will produce clusters, where the cluster members have a high level of similarity. Improving the performance of the K-Means cluster will be applied to determining the number of clusters using the elbow method.
Subject
General Physics and Astronomy
Reference6 articles.
1. SISTEM APLIKASI BERBASIS OPTIMASI METODE ELBOW UNTUK PENENTUAN CLUSTERING PELANGGAN;Muningsih;Joutica,2018
2. PENGEMBANGAN SISTEM ANALISIS AKADEMIS MENGGUNAKAN OLAP DAN DATA CLUSTERING STUDI KASUS : AKADEMIK UNIVERSITAS SEBELAS MARET SURAKARTA;Bakhtiar;J. Teknol. Inf. ITSmart,2016
Cited by
136 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献