John L. Pfaltz and Christopher M. Taylor. Uncovering Logical Implications in Scientific Databases through Empirical Induction. In ACM SIGMOD Conference, page (in review), Madison, WI, 2002.  Home/Search   Document Not in Database   Summary   Related Articles

....non empty intersections which are not already in L are recursively entered. Pseudo code for both these procedures is given in Appendix A. Experiments with various data sets have shown (a) that updates are local , in the sense that they are confined to a relatively small portion of the lattice [25, 28]; b) that the number of closed concepts is typically one to two orders of magnitude fewer than frequent sets [39] c) that extraction of specific rules of interest is fairly straight forward [28] and (d) that the current implementation is relatively slow. Clearly, an improved implementation of ....

.... are local , in the sense that they are confined to a relatively small portion of the lattice [25, 28] b) that the number of closed concepts is typically one to two orders of magnitude fewer than frequent sets [39] c) that extraction of specific rules of interest is fairly straight forward [28]; and (d) that the current implementation is relatively slow. Clearly, an improved implementation of this process is needed. 21 6.4 Output from mushroom Data Set We exercised these procedures on the mushroom data set (Section 9.2) because the properties of plant life tend to be deterministic. ....

John L. Pfaltz and Christopher M. Taylor. Uncovering Logical Implications in Scientific Databases through Empirical Induction. In ACM SIGMOD Conference, page (in review), Madison, WI, 2002.

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