National Academy of Agricultural Sciences (NAAS)
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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 |
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.
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