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Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items.
In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions.
In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge.
The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules.
Our innovation is to directly construct refinable bivariate spline function vectors with minimum supports and highest approximation orders on the six-directional mesh, and to compute their refinement masks which give rise to the matrix-valued coefficient stencils for the surface subdivision schemes.
Similar(55)
Reduce the minimum support, and go to step 6. .
These algorithms are based primarily on minimum support and the minimum confidence.
Two measures are required: the minimum support and the minimum confidence, Table 6.
To extract the association rules, two measures are required: the minimum support and the minimum confidence.
Besides the traditional minimum support and confidence, two new constraints, schema and opportunistic confidence, are considered.
After obtaining large itemsets, the association rules are generated based on minimum support and minimum confidence value.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com