图 书 馆
流失预测模型在零售银行，利用模糊C-均值聚类Churn Prediction Model in Retail Banking Using Fuzzy C-Means Clustering |
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| 课程网址: | http://videolectures.net/sikdd08_popovic_cpm/ |
| 主讲教师: | Džulijana Popović |
| 开课单位: | 领英公司 |
| 开课时间: | 2008-11-07 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The paper presents model based on fuzzy methods for churn prediction in retail banking. The study was done on the real, anonymised data of 5000 clients of a retail bank. Real data are great strength of the study, as a lot of studies often use old, irrelevant or artificial data. Canonical discriminant analysis was applied to reveal variables that provide maximal separation between clusters of churners and non-churners. Combination of standard deviation, canonical discriminant analysis and k-means clustering results were used for outliers detection. Due to the fuzzy nature of practical customer relationship management problems it was expected, and shown, that fuzzy methods performed better than the classical ones. According to the results of the preliminary data exploration and fuzzy clustering with different values of the input parameters for fuzzy c-means algorithm, the best parameter combination was chosen and applied to training data set. Four different prediction models, called prediction engines, have been developed. The definitions of clients in the fuzzy transitional conditions and the distance of k instances fuzzy sums were introduced. The prediction engine using these sums performed best in churn prediction, applied to both balanced and non-balanced test sets. |
| 关 键 词: | 计算机科学; 模糊逻辑; 聚类算法 |
| 课程来源: | 视频讲座网公开课 |
| 最后编审: | 2020-06-04：dingaq |
| 阅读次数: | 54 |