Research on clustering algorithms based on the Iris dataset

Abstract

In the burgeoning field of machine learning, clustering algorithms play a quintessential role in uncovering hidden patterns and structures within data. This study commences by highlighting the critical importance of machine learning and the expansive application of clustering algorithms across various disciplines. It then provides a brief background, tracing the development history of clustering algorithms and elucidating the unique characteristics and methodologies inherent to different algorithms. Through empirical analysis conducted on the Iris dataset, this research evaluates the performance of the K-means, hierarchical clustering, and DBSCAN algorithms, leveraging experimental charts and datasets for a nuanced assessment. The comparative analysis reveals distinct advantages and disadvantages of each algorithm, facilitating a balanced discussion on their practical implications. The conclusion synthesizes these findings, offering insights into the comparative merits of the algorithms and suggesting avenues for future research. This investigation aims to deepen the comprehension of the application challenges and opportunities presented by clustering algorithms, thus offering a guiding framework for future explorations in the field.