Authors :
Sunny Kumar; Prince Mewada; Aishwarya
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/415GhBE
DOI :
https://doi.org/10.5281/zenodo.7894496
Abstract :
- A vital data mining method for analysing
large records is clustering. Utilising clustering
techniques for enormous data presents hurdles in
addition to potential new issues brought on by massive
datasets. The question is how to deal with this hassle and
how to install clustering techniques to big data and get
the results in a reasonable amount of time given that
large information is related to terabytes and petabytes of
information and clustering algorithms are come with
excessive computational costs. This paper aims to
evaluate the design and development of agglomeration
algorithms to address vast knowledge difficulties,
starting with initially proposed algorithms and ending
with contemporary unique solutions The techniques and
the key challenges for developing advanced clustering
algorithms are introduced and examined, and
afterwards the potential future route for more advanced
algorithms is based on computational complexity. In this
study, we address big data applications for actual world
objects and clustering techniques.
Keywords :
Big Data, Clustering Algorithms, Computational complexity, Partition based Algorithms, Hierarchical Algorithms.
- A vital data mining method for analysing
large records is clustering. Utilising clustering
techniques for enormous data presents hurdles in
addition to potential new issues brought on by massive
datasets. The question is how to deal with this hassle and
how to install clustering techniques to big data and get
the results in a reasonable amount of time given that
large information is related to terabytes and petabytes of
information and clustering algorithms are come with
excessive computational costs. This paper aims to
evaluate the design and development of agglomeration
algorithms to address vast knowledge difficulties,
starting with initially proposed algorithms and ending
with contemporary unique solutions The techniques and
the key challenges for developing advanced clustering
algorithms are introduced and examined, and
afterwards the potential future route for more advanced
algorithms is based on computational complexity. In this
study, we address big data applications for actual world
objects and clustering techniques.
Keywords :
Big Data, Clustering Algorithms, Computational complexity, Partition based Algorithms, Hierarchical Algorithms.
