Selection for Franchise Restaurant Industry Using Visualization Data Mining Techniques
碩士 === 元智大學 === 經營管理碩士班(企業管理與服務科學學程) === 102 === In this study, we aim to find a store location selection model which can better explain the relation between sales revenue and store evaluation factors. Evaluation data come from 3 sets of sources, site factors, ERP financial data, and some open data....
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| Format: | Others |
| Language: | zh-TW |
| Online Access: | http://ndltd.ncl.edu.tw/handle/688d2s |
| Summary: | 碩士 === 元智大學 === 經營管理碩士班(企業管理與服務科學學程) === 102 === In this study, we aim to find a store location selection model which can better explain the relation between sales revenue and store evaluation factors. Evaluation data come from 3 sets of sources, site factors, ERP financial data, and some open data. This study uses the K-means analysis, KNN classification method and simple Bayesian classification to build a model to select shop location. K-means clustering analysis method is divided similar characters of the data into the same group. KNN classification must separate the data into class first; new data need to separate in accordance with the characteristics of class. Simple Bayesian classification separates the data by probability approach. The results show that a simple Bayesian classification has best result. In addition, numbers of people passing and rents have greater impact on sales revenue.
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