A text mining agents based architecture for personal e-mail filtering and management

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 e-mail filtering and management.