Let G be an undirected connected graph that is not a path. We define h(G) (respectively, s(G)) to be the least integer m such that the iterated line graph Lm(G) has a Hamiltonian cycle (respectively, a spanning closed trail). To obtain upper bounds on h(G) and s(G), we characterize the least integer m such that Lm(G) has a connected subgraph H, in which each edge of H is in a 3‐cycle and V(H) contains all vertices of degree not 2 in Lm(G). We characterize the graphs G such that h(G) — 1 (respectively, s(G)) is greater than the radius of G.
The first and foremost task in any associative classification algorithm is mining of the association rules. Many studies have shown that the minimum support measure plays a key role in building an accurate classifier. Without the knowledge of the items and their frequency, user specified support measures are inappropriate, seldom may they coincide. .In this paper, we propose an approach called DASApriori i.e) Dynamic Adaptive Support Apriori to calculate the minimum support for mining class association rules and to build a simple and accurate classifier. Our experiments on 5 databases from UCI repository show that it achieves the best balance between the rule set size and classification accuracy even without the use of rule pruning techniques when compared with other associative classification approaches.
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