Pengklusteran Data Iris Menggunakan Metode Fuzzy C-Means
Abstract
This study focuses on the application of the Fuzzy C-Means method for clustering the Iris dataset. Clustering is a widely used technique for grouping similar data objects together, and the Iris dataset, which consists of measurements of iris flowers, has been a popular choice for clustering analysis. The Fuzzy C-Means algorithm, based on fuzzy logic, allows for a more flexible and nuanced approach to clustering by assigning degrees of membership to data points, capturing the inherent uncertainty and ambiguity in the dataset. By utilizing fuzzy logic, the Fuzzy C-Means method aims to accurately classify iris flowers into distinct clusters based on their petal width, petal length, sepal width, and sepal length. The results of this study contribute to the understanding of fuzzy clustering techniques and their application in pattern recognition and data analysis.
Keywords: Iris dataset, clustering, Fuzzy C-Means, fuzzy logic.
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