Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient dynamic mining of constrained frequent sets
ACM Transactions on Database Systems (TODS)
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
Visual Analysis Tool for Bipartite Networks
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
LogView: Visualizing Event Log Clusters
PST '08 Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust
WiFIsViz: Effective Visualization of Frequent Itemsets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Organization Diagnosis Tools Based on Social Network Analysis
Proceedings of the Symposium on Human Interface 2009 on ConferenceUniversal Access in Human-Computer Interaction. Part I: Held as Part of HCI International 2009
Honeycomb: Visual Analysis of Large Scale Social Networks
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Augmented cognition, universal access and social intelligence in the information society
FAC'07 Proceedings of the 3rd international conference on Foundations of augmented cognition
FIsViz: a frequent itemset visualizer
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
FpVAT: a visual analytic tool for supporting frequent pattern mining
ACM SIGKDD Explorations Newsletter
CloseViz: visualizing useful patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Analyzing Social Media Networks with NodeXL: Insights from a Connected World
Analyzing Social Media Networks with NodeXL: Insights from a Connected World
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Exploring Social Networks: A Frequent Pattern Visualization Approach
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Mining social networks for significant friend groups
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
A constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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Co-authorship networks are examples of social networks, in which researchers are linked by their joint publications. Like many other instances of social networks, co-authorship networks contain rich sets of valuable data. In this paper, we propose a visual analytic tool, called SocialVis, to analyze and visualize these networks. In particular, SocialVis first applies frequent pattern mining to discover implicit, previously unknown and potential useful social information such as teams of multiple frequently collaborating researchers, their composition, and their collaboration frequency. SocialVis then uses a visual representation to present the mined social information so as to help users get a better understanding of the networks.