Solving the maximum clique problem using a tabu search approach
Annals of Operations Research - Special issue on Tabu search
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
GHIC: A Hierarchical Pattern-Based Clustering Algorithm for Grouping Web Transactions
IEEE Transactions on Knowledge and Data Engineering
Scan Statistics on Enron Graphs
Computational & Mathematical Organization Theory
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Proceedings of the 2006 international workshop on Mining software repositories
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Detect community structure from the Enron Email Corpus Based on Link Mining
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Discovering Frequent Closed Partial Orders from Strings
IEEE Transactions on Knowledge and Data Engineering
Who Thinks Who Knows Who? Socio-cognitive Analysis of Email Networks
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A segmentation-based approach for temporal analysis of software version repositories
Journal of Software Maintenance and Evolution: Research and Practice
Efficient algorithms for segmentation of item-set time series
Data Mining and Knowledge Discovery
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Finding hidden group structure in a stream of communications
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Efficient algorithms for constructing time decompositions of time stamped documents
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
TOD: Temporal outlier detection by using quasi-functional temporal dependencies
Data & Knowledge Engineering
Towards event ordering in digital forensics
Proceedings of the 12th ACM workshop on Multimedia and security
Expertise ranking using activity and contextual link measures
Data & Knowledge Engineering
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Many kinds of information are hidden in email data, such as the information being exchanged, the time of exchange, and the user IDs participating in the exchange. Analyzing the email data can reveal valuable information about the social networks of a single user or multiple users, the topics being discussed, and so on. In this paper, we describe a novel approach for temporally analyzing the communication patterns embedded in email data based on time series segmentation. The approach computes egocentric communication patterns of a single user, as well as sociocentric communication patterns involving multiple users. Time series segmentation is used to uncover patterns that may span multiple time points and to study how these patterns change over time. To find egocentric patterns, the email communication of a user is represented as an item-set time series. An optimal segmentation of the item-set time series is constructed, from which patterns are extracted. To find sociocentric patterns, the email data is represented as an item-setgroup time series. Patterns involving multiple users are then extracted from an optimal segmentation of the item-setgroup time series. The proposed approach is applied to the Enron email data set, which produced very promising results.