Andere Kunden interessierten sich auch für
![Paralleler K-Means-Algorithmus basierend auf Hadoop-MapReduce für Data Mining]( "Paralleler K-Means-Algorithmus basierend auf Hadoop-MapReduce für Data Mining")
Paralleler K-Means-Algorithmus basierend auf Hadoop-MapReduce für Data Mining39,90 €![Algoritmo K-Means parallelo basato su Hadoop-MapReduce per il data mining]( "Algoritmo K-Means parallelo basato su Hadoop-MapReduce per il data mining")
Algoritmo K-Means parallelo basato su Hadoop-MapReduce per il data mining26,99 €![Algorithme K-Means parallèle basé sur Hadoop-MapReduce pour l'exploration de données]( "Algorithme K-Means parallèle basé sur Hadoop-MapReduce pour l'exploration de données")
Algorithme K-Means parallèle basé sur Hadoop-MapReduce pour l'exploration de données26,99 €![Algorytm K-Means równoleg¿y oparty na Hadoop-MapReduce do eksploracji danych]( "Algorytm K-Means równoleg¿y oparty na Hadoop-MapReduce do eksploracji danych")
Algorytm K-Means równoleg¿y oparty na Hadoop-MapReduce do eksploracji danych26,99 €![Algoritmo K-Means paralelo baseado em Hadoop-MapReduce para Mineração de dados agrícolas]( "Algoritmo K-Means paralelo baseado em Hadoop-MapReduce para Mineração de dados agrícolas")
Algoritmo K-Means paralelo baseado em Hadoop-MapReduce para Mineração de dados agrícolas26,99 €![Data Mining for Multimedia Databases]( "Data Mining for Multimedia Databases")
Data Mining for Multimedia Databases45,99 €![Regulations for the Concession of Mining for the Exploitation of Mineral Water]( "Regulations for the Concession of Mining for the Exploitation of Mineral Water")
Regulations for the Concession of Mining for the Exploitation of Mineral Water27,99 €
This work aimed to investigate the use of a parallel K-Means clustering algorithm, based on the MapReduce programming model, to improve the response time of data mining. The algorithm's performance was evaluated in terms of SpeedUp and ScaleUp. To this end, experiments were performed on a Hadoop cluster consisting of six computers with standard hardware. The clustered data are measurements from flow towers in agricultural regions and belong to Ameriflux. The experiments were performed using 3, 4, and 6 machines, respectively. The results showed that with the increase in the number of machines, there was a gain in performance, with the best time obtained using six machines, reaching a SpeedUp of 3.25. It was found that the application scales well with the equivalent increase in data size and number of machines in the cluster, achieving similar performance in the tests.