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Big Data Mining Based on Computational Intelligence and Fuzzy Clustering

Big Data Mining Based on Computational Intelligence and Fuzzy Clustering

Usman Akhtar, Mehdi Hassan
Copyright: © 2019 |Pages: 18
ISBN13: 9781522575016|ISBN10: 1522575014|EISBN13: 9781522575023
DOI: 10.4018/978-1-5225-7501-6.ch024
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MLA

Akhtar, Usman, and Mehdi Hassan. "Big Data Mining Based on Computational Intelligence and Fuzzy Clustering." Web Services: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 413-430. https://doi.org/10.4018/978-1-5225-7501-6.ch024

APA

Akhtar, U. & Hassan, M. (2019). Big Data Mining Based on Computational Intelligence and Fuzzy Clustering. In I. Management Association (Ed.), Web Services: Concepts, Methodologies, Tools, and Applications (pp. 413-430). IGI Global. https://doi.org/10.4018/978-1-5225-7501-6.ch024

Chicago

Akhtar, Usman, and Mehdi Hassan. "Big Data Mining Based on Computational Intelligence and Fuzzy Clustering." In Web Services: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 413-430. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7501-6.ch024

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Abstract

The availability of a huge amount of heterogeneous data from different sources to the Internet has been termed as the problem of Big Data. Clustering is widely used as a knowledge discovery tool that separate the data into manageable parts. There is a need of clustering algorithms that scale on big databases. In this chapter we have explored various schemes that have been used to tackle the big databases. Statistical features have been extracted and most important and relevant features have been extracted from the given dataset. Reduce and irrelevant features have been eliminated and most important features have been selected by genetic algorithms (GA). Clustering with reduced feature sets requires lower computational time and resources. Experiments have been performed at standard datasets and results indicate that the proposed scheme based clustering offers high clustering accuracy. To check the clustering quality various quality measures have been computed and it has been observed that the proposed methodology results improved significantly. It has been observed that the proposed technique offers high quality clustering.

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