Knowledge discovery from databases with the guidance of a causal network
The advancement of knowledge discovery from databases (KDD) has been hampered by the problems such as the lack of statistical rigor, overabundance of patterns, and poor integration. This paper describes a new model for KDD that applies a causal network to guide the discovery processes. The new model not only allows the user to express what kind of knowledge to be discovered, but also uses the user intention to alleviate the overabundance problem. In this new model, the causal network is applied to represent the relevant variables and their relationships in the problem domain, and in due course updated according to the extracted knowledge. An interactive data mining process based on this model is described. The approach allows a knowledge discovery process to be conducted in a more controllable manner. Fundamental features of the new model are discussed, and an example is provided to illustrate the discovery processes using this model.
KeywordsKnowledge discovery from databases Causal networks Goal-driven
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