A Text Mining Agents Based Architecture for Personal E-mail Filtering and Management

Authors:
Ning Zhong;Takahisa Matsunaga;Chunnian Liu
Affiliations:
-;-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

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Abstract

E-mail messages can be modeled as semi-structured documents that consist of a set of classes and a number of variable length free-text. Thus, many text mining techniques can be used to develop a personal e-mail filtering and management system. This paper addresses a text mining agents based architecture, in which two kinds of text mining agents: USPC (uncertainty sampling based probabilistic classifier) and R2L (rough relation learning) are used cooperatively, for personal email filtering and management.