Clustering Analysis on the Iris Dataset

DOI

No DOI

Published In

Volume 6, Issue 1 (2026)

Published Online

January-February 2026

Pages

82-92

Article Type

Original Article

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Clustering is an unsupervised learning technique used to identify patterns and groupings in data. This research paper investigates clustering methods applied to the Iris dataset, a classical dataset in the field of machine learning. By employing k-means clustering techniques, we aim to group the iris flowers based on their morphological features. The results demonstrate the efficacy of clustering techniques in distinguishing patterns and provide insights into the structure of the dataset. We also introduce a machine learning model to evaluate the quality of clustering and analyse the findings. This research provides a detailed comparison of clustering algorithms, evaluates their performance using metrics like Silhouette Score, and explores their practical implications in data analysis.