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2020 | OriginalPaper | Chapter

A Generalized Study on Data Mining and Clustering Algorithms

Authors : Syed Thouheed Ahmed, S. Sreedhar Kumar, B. Anusha, P. Bhumika, M. Gunashree, B. Ishwarya

Published in: New Trends in Computational Vision and Bio-inspired Computing

Publisher: Springer International Publishing

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Abstract

Data Mining is the procedure of extracting information from a data set and transforms information into comprehensible structure for processing. Clustering is data mining technique used to process data elements into their related groups or partition. Thus, the process of partitioning data objects into subclasses is term as ‘cluster’. It consists of data objects with un-unified proposition of high inter similarity and low intra similarity ratios. Thus reflecting the quality of cluster depends on the methods used. Clustering also called data segmentation, divides huge data sets into several groups based upon their similarities. This paper discusses a literature study of various clustering techniques and their comparison on key issues to give guidance for choosing clustering algorithm for a specific research application. The comparison is based on computing performance and clustering accuracy.

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Metadata
Title
A Generalized Study on Data Mining and Clustering Algorithms
Authors
Syed Thouheed Ahmed
S. Sreedhar Kumar
B. Anusha
P. Bhumika
M. Gunashree
B. Ishwarya
Copyright Year
2020
Publisher
Springer International Publishing
Book
New Trends in Computational Vision and Bio-inspired Computing

Print ISBN: 978-3-030-41861-8

Electronic ISBN: 978-3-030-41862-5

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-41862-5_114

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