Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

2nd International Conference on Vocational Education and Electrical Engineering

无线电电子学;教育

Syakur, M.A.^1 ; Khotimah, B.K.^1 ; Rochman, E.M.S.^1 ; Satoto, B.D.^1

Faculty of Engineering, University of Trunojoyo Madura, Indonesia^1

关键词: Customer profiles;    Customer profiling;    K;    means clustering;    K-Means clustering algorithm;    K-means clustering method;    Large amounts of data;    Number of clusters;    Optimization method;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/336/1/012017/pdf
DOI  :  10.1088/1757-899X/336/1/012017

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.