The important methods of data mining is large and from these methods is mining of association rule. The miningof association rule gives huge number of the rules. These huge rules make analyst consuming more time when searchingthrough the large rules for finding the interesting rules. One of the solutions for this problem is combing between one of theAssociation rules visualization method and generalization method. Association rules visualization method is graph-basedmethod. Generalization method is Attribute Oriented Induction algorithm (AOI). AOI after combing calls ModifiedAOI because it removes and changes in the steps of the traditional AOI. The graph technique after combing also callsgrouped graph method because it displays the aggregated that results rules from AOI. The results of this paper are ratio ofcompression that gives clarity of visualization. These results provide the ability for test and drill down in the rules orunderstand and roll up.
The best lower bound known on the crossing number of the complete bipartite graph is : $$cr(K_{m,n}) \geq (1/5)(m)(m-1)\lfloor n/2 \rfloor \lfloor(n-1)/2\rfloor$$ In this paper we prove that: $$cr(K_{m,n}) \geq (1/5)m(m-1)\lfloor n/2 \rfloor \lfloor (n-1)/2 \rfloor + 9.9 \times 10^{-6} m^2n^2$$ for sufficiently large $m$ and $n$.
Semi-bipartite Graph Visualization for Gene Ontology Networks
