원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.9 No.9
2014.09
pp.281-298
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Customer churn warning prediction is one of the most important problems in customer relationship management (CRM). Its aim is to monitor customer loss conditions, explore for internal loss rules of mass transaction data, give early warning about customer loss inclination and retain valuable customers to maximize the profit of a company. Under the circumstance of rapidly emerging customer churn warning research under the big data background, we attempt to explore for mass security customer data and to focus on analysis and design on customer churn warning model based on data mining technologies and the theory of customer churn management process.
목차
Abstract
1. Introduction
2. Background Knowledge
2.1. C5.0 Algorithm
2.2. K-Means Algorithm
3. Data Pre-Processing
3.1. Data Description
3.2. Data Cleaning
3.3. Data Combination
3.4. Variable Management
3.5. Sample Management
3.6. Importance of Variables
4. Model of Customer Churn Warning
4.1. Set the Model Parameter
4.2. Data Flow
4.3. Results
5. Segmentation Model of Customer Churn
5.1. Data Flow of Segmentation Model
5.2. Results of Customer Churn Segmentation
6. Model Evaluation
6.1. Model of Customer Churn Warning
6.2. Model of Customer Churn Segmentation
7. Conclusions
References
1. Introduction
2. Background Knowledge
2.1. C5.0 Algorithm
2.2. K-Means Algorithm
3. Data Pre-Processing
3.1. Data Description
3.2. Data Cleaning
3.3. Data Combination
3.4. Variable Management
3.5. Sample Management
3.6. Importance of Variables
4. Model of Customer Churn Warning
4.1. Set the Model Parameter
4.2. Data Flow
4.3. Results
5. Segmentation Model of Customer Churn
5.1. Data Flow of Segmentation Model
5.2. Results of Customer Churn Segmentation
6. Model Evaluation
6.1. Model of Customer Churn Warning
6.2. Model of Customer Churn Segmentation
7. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보