Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Data mining using links in open hypermedia
MIS'02 Proceedings of the 2002 international conference on Metainformatics
Research issues in mining multiple data streams
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
On classifying drifting concepts in P2P networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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We present a collective approach to mine Bayesian net-works from distributed heterogenous web-log data streams. In this approach we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits asub-set of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmittedfrom the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian net-work, that models the entire data. We applied this techniqueto mine multiple data streams where data centralization is difficult because of large response time and scalability issues.Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.