Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data

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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Big Data Mining Based on Computational Intelligence and Fuzzy Clustering

Web Services ◽

10.4018/978-1-5225-7501-6.ch024 ◽

2019 ◽

pp. 413-430

Author(s):

Usman Akhtar ◽

Mehdi Hassan

Keyword(s):

Big Data ◽

Computational Intelligence ◽

Clustering Algorithms ◽

Heterogeneous Data ◽

Computational Time ◽

Statistical Features ◽

Feature Sets ◽

Clustering Quality ◽

The Given ◽

Different Sources

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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Uncertainty-Based Clustering Algorithms for Large Data Sets

Modern Technologies for Big Data Classification and Clustering - Advances in Data Mining and Database Management ◽

10.4018/978-1-5225-2805-0.ch001 ◽

2018 ◽

pp. 1-33 ◽

Cited By ~ 1

Author(s):

B. K. Tripathy ◽

Hari Seetha ◽

M. N. Murty

Keyword(s):

Big Data ◽

Data Clustering ◽

Clustering Algorithms ◽

Large Data ◽

Large Data Sets ◽

Mining Machine ◽

Data Sets ◽

Fuzzy C Means ◽

Intuitionistic Fuzzy ◽

New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

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A survey on parallel clustering algorithms for Big Data

Artificial Intelligence Review ◽

10.1007/s10462-020-09918-2 ◽

2020 ◽

Author(s):

Zineb Dafir ◽

Yasmine Lamari ◽

Said Chah Slaoui

Keyword(s):

Big Data ◽

Clustering Algorithms ◽

Parallel Clustering

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