Stream mining with big data architecture by Automated swarm search technique
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Big data is a leading enabled technology by recent advances in technologies and architecture. However, big data is facing the problem of hardware and processing resources costs, by adoption costs of big data technology prohibitive to small and medium sized businesses. Cloud based big data servers is a set of it services that are provided to a considering the over a network on a leased basis and with the ability to scale up or down their service requirements. because of using cloud as a service process the advantages includes scalability, resilience, flexibility, efficiency and outsourcing non-core activities. The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.
Keywords: Feature Selection, Swarm Intelligence, Classification, Big Data, Particle Swarm Optimization.
Citation: *, ( 2018), Stream mining with big data architecture by Automated swarm search technique. Scientific Transactions in Environment and Technovation Journal(STET), 11(4): 183-187
Received: 06/07/2017; Accepted: 03/13/2018;
*Correspondence: unknow user, unknow user