Email overload: exploring personal information management of email
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
IEEE Intelligent Systems
Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Classification of Web Documents Using a Graph Model
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Contextual search and name disambiguation in email using graphs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Information Systems (TOIS)
Graph homomorphism revisited for graph matching
Proceedings of the VLDB Endowment
Automatically tagging email by leveraging other users' folders
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.02 |
In this paper we propose a novel approach that uses structure as well as the content of emails in a folder for email classification. Our approach is based on the premise that representative — common and recurring — structures/patterns can be extracted from a pre-classified email folder and the same can be used effectively for classifying incoming emails. A number of factors that influence representative structure extraction and the classification are analyzed conceptually and validated experimentally. In our approach, the notion of inexact graph match is leveraged for deriving structures that provide coverage for characterizing folder contents. Extensive experimentation validate the selection of parameters and the effectiveness of our approach for email classification.