association rules optimization using de sharma pankaj (8 resultados)

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Taschenbuch. Condición: Neu. Association Rules Optimization using ABC Algorithm with Mutation | Pankaj Sharma (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202680349 | Verantwortliche Person für die EU: LAP Lambert Academic Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In data mining, association rule mining is one of the popular and simple methods to find frequent itemsets from a large dataset. While generating frequent itemsets from a large dataset using association rule mining, the computer takes too much time. This can be improved by using an artificial bee colony algorithm (ABC). The artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, an artificial bee colony algorithm with a mutation operator is used to generate high-quality association rules for finding frequent itemsets from large data sets. The mutation operator is used after the scout bee phase in this work. In general, the rule generated by the association rule mining technique does not consider the negative occurrences of attributes in them, but by using an artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contain negative attributes. 76 pp. Englisch.

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Condición: New. PRINT ON DEMAND.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma PankajProf. Pankaj Sharma has completed his M.Tech degree in computer science and engineering, having 9 years of teaching experience. He has published various papers in Scopus indexed conferences and international journals. He.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In data mining, association rule mining is one of the popular and simple methods to find frequent itemsets from a large dataset. While generating frequent itemsets from a large dataset using association rule mining, the computer takes too much time. This can be improved by using an artificial bee colony algorithm (ABC). The artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, an artificial bee colony algorithm with a mutation operator is used to generate high-quality association rules for finding frequent itemsets from large data sets. The mutation operator is used after the scout bee phase in this work. In general, the rule generated by the association rule mining technique does not consider the negative occurrences of attributes in them, but by using an artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contain negative attributes.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In data mining, association rule mining is one of the popular and simple methods to find frequent itemsets from a large dataset. While generating frequent itemsets from a large dataset using association rule mining, the computer takes too much time. This can be improved by using an artificial bee colony algorithm (ABC). The artificial bee colony algorithm is an optimization algorithm based on the foraging behavior of artificial honey bees. In this paper, an artificial bee colony algorithm with a mutation operator is used to generate high-quality association rules for finding frequent itemsets from large data sets. The mutation operator is used after the scout bee phase in this work. In general, the rule generated by the association rule mining technique does not consider the negative occurrences of attributes in them, but by using an artificial bee colony algorithm (ABC) over these rules the system can predict the rules which contain negative attributes.