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Erschienen in:
Study and Analysis of Subthreshold Leakage Current in Sub-65 nm NMOSFET Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Hybrid Clustering Technique to Improve Big Data Accessibility Based on Machine Learning Approaches

verfasst von : E. Omid Mahdi Ebadati, Mohammad Mortazavi Tabrizi

Erschienen in: Information Systems Design and Intelligent Applications

Verlag: Springer India

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Abstract

Big data is called to a large or complex data from traditional ones, which is unstructured in many case. Accessing to a specific value in a huge data that is not sorted or organized can be time consuming and require a high processing. With growing of data, clustering can be a most important unsupervised approach that finds a structure for data. In this paper, we demonstrate two approaches to cluster data with high accuracy, and then we sort data by implementing merge sort algorithm finally, we use binary search to find a data value point in a specific range of data. This research presents a high value efficiency combo method in big data by using genetic and k-means. After clustering with k-means total sum of the Euclidean distances is 3.37233e+09 for 4 clusters, and after genetic algorithm this number reduce to 0.0300344 in the best fit. In the second and third stage we show that after this implementation, we can access to a particular data much faster and accurate than other older methods.

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Vorheriges Kapitel Bayesian-Fuzzy GIS Overlay to Construe Congestion Dynamics
Nächstes Kapitel Efficient Iceberg Query Evaluation in Distributed Databases by Developing Deferred Strategies
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Metadaten
Titel
A Hybrid Clustering Technique to Improve Big Data Accessibility Based on Machine Learning Approaches
verfasst von
E. Omid Mahdi Ebadati
Mohammad Mortazavi Tabrizi
Copyright-Jahr
2016
Verlag
Springer India
Buch
Information Systems Design and Intelligent Applications

Print ISBN: 978-81-322-2753-3

Electronic ISBN: 978-81-322-2755-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-81-322-2755-7_43

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