Mining class outliers: concepts, algorithms and applications in CRM |
| |
Authors: | Zengyou He Xiaofei Xu Joshua Zhexue Huang Shengchun Deng |
| |
Affiliation: | aDepartment of Computer Science and Engineering, Harbin Institute of Technology, 92 West Dazhi Street, P.O. Box 315, Harbin 150001, China bE-Business Technology Institute, The University of Hong Kong, Pokfulam, Hong Kong, China |
| |
Abstract: | Outliers, or commonly referred to as exceptional cases, exist in many real-world databases. Detection of such outliers is important for many applications and has attracted much attention from the data mining research community recently. However, most existing methods are designed for mining outliers from a single dataset without considering the class labels of data objects. In this paper, we consider the class outlier detection problem ‘given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels’. By generalizing two pioneer contributions [Proc WAIM02 (2002); Proc SSTD03] in this field, we develop the notion of class outlier and propose practical solutions by extending existing outlier detection algorithms to this case. Furthermore, its potential applications in CRM (customer relationship management) are also discussed. Finally, the experiments in real datasets show that our method can find interesting outliers and is of practical use. |
| |
Keywords: | Outlier Data mining CRM Direct marketing |
本文献已被 ScienceDirect 等数据库收录! |
|