Loi de puissance et caractérisation des réseaux dynamiques

National audienceCaractériser la dynamique et l'évolution au cours du temps d'un réseau sans fil multi sauts (réseaux ad hoc, réseaux de senseurs) apparaît comme un problème important car cela doit permettre de comprendre, de modéliser et de développer des algorithmes et des protocoles adaptés. A partir des traces expérimentales collectées lors de la conférence Infocom 2005 [1], nous proposons une analyse plus avancée de la structure évolutive de ce genre de réseaux. Plus précisément, nous montrons que la seule caractérisation au travers d'une loi de puissance des contacts et inter-contacts entre individus/noeuds n'est pas suffisante pour capturer et rendre l'évolution du réseau. A partir d'une analyse combinant trois approches [2] qui se révèlent très complémentaires (théorie des graphes, processus aléatoires et fouille de données), nous proposons un modèle simple mettant en évidence la complexité de la structure évolutive

Extraction of High Utility Itemsets using Utility Pattern with Genetic Algorithm from OLTP System

To analyse vast amount of data, Frequent pattern mining play an important role in data mining. In practice, Frequent pattern mining cannot meet the challenges of real world problems due to items differ in various measures. Hence an emerging technique called Utility-based data mining is used in data mining processes.The utility mining not only considers the frequency but also see the utility associated with the itemsets.The main objective of utility mining is to extract the itemsets with high utilities, by considering user preferences such as profit,quantity and cost from OLTP systems. In our proposed approach, we are using UP growth with Genetic Algorithm. The idea is that UP growth algorithm would generate Potentially High Utility Itemsets and Genetic Algorithm would optimize and provide the High Utility Item set from it. On comparing with existing algorithm, the proposed approach is performing better in terms of memory utilization. DOI: 10.17762/ijritcc2321-8169.15039

Finding frequent closed itemsets with an extended version of the Eclat algorithm

Apriori is the most well-known algorithm for finding frequent itemsets (FIs) in a dataset. For generating interesting association rules, we also need the so-called frequent closed itemsets (FCIs) that form a subset of FIs. Apriori has a simple extension called Apriori-Close that can filter FCIs among FIs. However, it is known that vertical itemset mining algorithms outperform the Apriori-like levelwise algorithms. Eclat is another well-known vertical miner that can produce the same output as Apriori, i.e. it also finds the FIs in a dataset. Here we propose an extension of Eclat, called Eclat-Close that can filter FCIs among FIs. This way Eclat-Close can be used as an alternative of Apriori-Close. Experimental results show that Eclat-Close performs much better than Apriori-Close, especially on dense, highly-correlated datasets