A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from directed graph data by stepwise pair expansion (pairwise chunking). In this paper, we expand the capability of the Graph-Based Induction to handle not only tree structured data but also multi-inputs/outputs nodes and loop structure (including a self-loop) which cannot be treated in the conventional way. The method is verified to work as expected using artificially generated data and we evaluated experimentally the computation time of the implemented program. We, further, show the effectiveness of our approach by applying it to two kinds of the real-world data: World Wide Web browsing data and DNA sequence data.

“Ripple Down Rules (RDR)” Method is one of the promising approaches to directly acquire and encode knowledge from human experts. It requires data to be supplied incrementally to the knowledgebase being constructed and new piece of knowledge is added as an exception to the existing knowledge. Because of this patching principle, the knowledge acquired strongly depends on what is given as the default knowledge. Further, data are often noisy and we want the RDR noise resistant. This paper reports experimental results about the effect of the selection of default knowledge and the amount of noise in data on the performance of RDR using a simulated expert. The best default knowledge is characterized as the class knowledge that maximizes the minimum description length to encode rules and misclassified cases. This criterion also holds even when the data are noisy.

. The ‘Ripple Down Rules (RDR)’ method is a promising approach to directly acquiring and encoding knowledge from human experts. It requires data to be supplied incrementally to the knowledge base being constructed, each new piece of knowledge being added as an exception to the existing knowledge base. Because of this patching principle, the knowledge acquired depends strongly on what is given as the default knowledge, used as an implicit outcome when inference fails. Therefore, it is important to choose good default knowledge for constructing an accurate and compact knowledge base. Further, real-world data are often noisy and we want the RDR to be noise resistant. This paper reports experimental results about the effect of the selection of default knowledge and the amount of noise in data on the performance of RDR, using a simulated expert in place of a human expert. The best default knowledge is characterized as the class knowledge that maximizes the description length of encoding rules and misclassified cases. We confirmed by extensive experimentation that this criterion is indeed valid and useful in constructing an accurate and compact knowledge base. We also ascertained that the same criterion holds when the data are noisy.

\Ripple Down Rules (RDR)" Method is one of the promising approaches to directly acquire and encode knowledge from human experts. It requires data to be supplied incrementally to the knowledgebase being constructed and new piece of knowledge is added as an exception to the existing knowledge. Because of this patching principle, the knowledge acquired strongly depends on what is given as the default knowledge. Further, data are often noisy and we want the RDR noise resistant. This paper reports experimental results about the e ect of the selection of default knowledge and the amount of noise in data on the performance of RDR using a simulated expert. The best default knowledge is characterized as the class knowledge that maximizes the minimum description length to encode rules and misclassi ed cases. The e ect of noise is sensitive at an earlier stage of knowledge acquisition where its performance strongly depends on the selection of default knowledge, but RDR eventually converges to its stable performance although the size of the knowledge base is strongly a ected by the chosen default knowledge.