Title :
Research for Customer Segmentation of Medical Insurance Based on K-means and C&R Tree Algorithms
Author_Institution :
Inst. of Math., Zhejiang Wanli Univ., Ningbo, China
Abstract :
This paper applies such data mining techniques as clustering and classifying to customer segmentation based on insurance Customer Risk Contribution matrix. A solution for segmentation management based on Clementine is put forwarded. It is brought forward that the insurance customer segmentation method, which can provide decision bases for insurance companies´ making premium rate and controlling claim risk. At the same time, it makes segmentation management more scientific and lifts the capability of insurance market competition.
Keywords :
customer relationship management; data mining; decision trees; insurance; pattern classification; pattern clustering; Clementine; claim risk control; data mining; insurance company; insurance customer risk contribution matrix; insurance customer segmentation; insurance market competition; k-means; medical insurance; premium rate; segmentation management; tree algorithm; C&R Tree; Customer Segmentation; K-means; Medical Insurance;
Conference_Titel :
Semantics Knowledge and Grid (SKG), 2010 Sixth International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-8125-5
Electronic_ISBN :
978-0-7695-4189-1
DOI :
10.1109/SKG.2010.59
