A Framework for Post-Rule Mining of Distributed Rule Bases

M. Muyeba and R. Mamadapali (UK)


Rule, query, distributed, induction, filtering, performance


Inductive algorithms produce rules that are used to evaluate and interpret their interestingness while some use rule querying as a post data mining technique for query optimisation purposes. The need for interactive and user query tools demand a data mining query language. The problem here addresses post rule based querying in a distributed environment. The generalization method of Attribute-Oriented Induction (AOI) with key-preservation (AOI-KP) is used to associate every rule with a collection of tuple keys at each host processor and utilizes a star schema database design. The key-preservation aspect seems to address the information loss problem in AOI and may help to perform efficient data queries using tuple keys. An SQL-like data and rule query operator that utilises data mining primitives in a distributed environment is presented with appropriate rule filtering mechanisms. Initial performance results indicate good scalability and speed-up in a distributed environment.

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