Automated detection of the first document reporting each new event in temporally-sequenced streams of documents is an open challenge. In this paper we propose a new approach which...
Yiming Yang, Jian Zhang, Jaime G. Carbonell, Chun ...
This paper focuses on the discovery of surprising, unexpected patterns, based on a data mining method that consists of detecting instances of Simpson's paradox. By its very n...
—Modern applications such as web knowledge base, network traffic monitoring and online social networks have made available an unprecedented amount of network data with rich type...
Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventio...
Minos N. Garofalakis, Rajeev Rastogi, Kyuseok Shim
The process of resource distribution and load balance of a distributed P2P network can be described as the process of mining Supplement Frequent Patterns (SFPs) from query transact...
Yintian Liu, Yingming Liu, Tao Zeng, Kaikuo Xu, Ro...