Data Mining for Predicting Customer Satisfaction Using Clustering Techniques

被引：0
|
作者：
Purwandari, Kartika [1 ]
Sigalingging, Join W. C. [2 ]
Fhadli, Muhammad [2 ]
Arizky, Shinta Nur [2 ]
Pardamean, Bens [3 ]
机构：
[1] Bina Nusantara Univ, Bioinformat & Data Sci Res Ctr, Jakarta 11480, Indonesia
[2] Natl Cent Univ, Dept Comp Sci & Informat Engn, Taoyuan, Taiwan
[3] Bina Nusantara Univ, BINUS Grad Program, Comp Sci, Comp Sci Dept, Jakarta 11480, Indonesia
来源：
PROCEEDINGS OF 2020 INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND TECHNOLOGY (ICIMTECH) | 2020年
关键词：
agglomerative clustering; customer satisfaction; data mining; K-means; spectral clustering;
D O I：
10.1109/icimtech50083.2020.9211272
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Managing customer satisfaction has become an important business trend, including restaurants business. This study aims to determine the application of the K-means, Spectral Clustering (SC), and Agglomerative Clustering (AC) method for measuring customer satisfaction on a family restaurant in Taiwan. We contribute the data collection process and application of data mining in a family restaurant. The clustering analysis based on agglomerative clustering approach performs as well as the K-means approach to cluster the same characteristics of the customers. At last, this study shows the measurement result of customer satisfaction and provides improvement suggestion to the restaurant concerned.
引用
收藏
页码：223 / 227
页数：5
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