Data Mining for Predicting Customer Satisfaction Using Clustering Techniques
| Autor: | Muhammad Fhadli, Kartika Purwandari, Bens Pardamean, Join W. C. Sigalingging, Shinta Nur Arizky |
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| Rok vydání: | 2020 |
| Předmět: |
Data collection
010504 meteorology & atmospheric sciences Computer science k-means clustering 02 engineering and technology Disease cluster computer.software_genre 01 natural sciences Spectral clustering Hierarchical clustering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Customer satisfaction Data mining Cluster analysis computer 0105 earth and related environmental sciences |
| Zdroj: | 2020 International Conference on Information Management and Technology (ICIMTech). |
| DOI: | 10.1109/icimtech50083.2020.9211272 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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