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By D. W. Albrecht, A. E. Nicholson, I. Zukerman (auth.), Xindong Wu, Ramamohanarao Kotagiri, Kevin B. Korb (eds.)
This booklet constitutes the refereed complaints of the second one Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The booklet offers 30 revised complete papers chosen from a complete of a hundred and ten submissions; additionally incorporated are 20 poster displays. The papers give a contribution new effects to all present facets in wisdom discovery and information mining at the study point in addition to at the point of structures improvement. one of the components lined are computer studying, info structures, the web, facts, wisdom acquisition, information visualization, software program reengineering, and data dependent systems.
Read or Download Research and Development in Knowledge Discovery and Data Mining: Second Pacific-Asia Conference, PAKDD-98 Melbourne, Australia, April 15–17, 1998 Proceedings PDF
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In subsequent iterations more clauses will appear until the stopping criterion is satisfied. Therefore recursive rules like the following appear: tag(A,B,art)← window(A,B,L1,L2,L3,L4,R1,R2,R3,R4),tag(A,L1,prep), tag(A,R1,n),tag(A,L2,n),tag(A,R2,virg),tag(A,L3,prep),!. tag(A,B,art)← window(A,B,L1,L2,L3,L4,R1,R2,R3,R4), word(A,B,a), tag(A,R2,prep), tag(A,R3,n),tag(A,R4,prep),!. 30 Alneu de Andrade Lopes and Alípio Jorge In general, the total number of iterations depends on the data, the language, and the underlying learning algorithm employed.
1998: “A CBR Architecture for Project Knowledge Management”, in Advances in CBR (Smyth B. ), Springer-Verlag. 47 3. Brachman R. , McGuinness D. , Patel-Schneider P.
The first four iterations use the CSC(RC1) algorithm and in the last one we use the algorithms CBR, RC1, and RC2 Figure 1 shows the coverage vs. error rate obtained using the CSC(ALG) in each iteration. In the first 4 iterations ALG is the rule learner RC1. In the last iteration we have used the algorithm CBR (Case Based Reasoning with an overlapping metric), RC1 with a new set of parameters of quality to answer the remaining cases, and RC2 (using explanations). The total number of iterations of the learning process depends on the data, the language, and the quality parameters (minimal confidence, support and selection algorithm).