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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(4): 2952-2961
DOI: https://doi.org/10.20546/ijcmas.2020.904.346


Clustering and Validation of Iris Flower Dataset using Relative Criteria
K. Sujatha1*, Vijayakumar Selvaraj2 and N. Nevashini3
1Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, India
2Manager Learning Analytics, Imarticus Learning Chennai, Chennai, Tamil Nadu, India
3Technology Lead-Data Science, iNurture Education Solutions Private Limited, Bengaluru, Karnataka, India
*Corresponding author
Abstract:

In the present work, attempts have been made to analyze the Iris data set with clustering technique which is the main task of exploratory data mining and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics. The Iris flower data set is a popular multivariate data set introduced by Sir Ronald Fisher as an example of discriminant analysis. The data on four characteristics of the three species of the Iris Flower, sepal length, sepal width, petal length and petal width has been taken from https://ieeexplore.ieee.org/document/771092 and has been analyzed using SAS software. Here we have extended the algorithm for better visualization of possible cluster structures and also to validate clusters. The optimal number of clusters was found in this dataset by using the four cluster validity indices viz., Dunn, DB, RMSSTD and RS indices which yield three and this is configurable to the real partitions of the dataset.


Keywords: Iris flower, Clustering, Dunn, DB, RMSSTD and RS index
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How to cite this article:

Sujatha, K., Vijayakumar Selvaraj and Nevashini, N. 2020. Clustering and Validation of Iris Flower Dataset using Relative Criteria.Int.J.Curr.Microbiol.App.Sci. 9(4): 2952-2961. doi: https://doi.org/10.20546/ijcmas.2020.904.346