🇮🇩 Karinda Amelia
LinkedIn | Github | **Instagram** | Email
Demo program : clustering-kmeans-kmedoids-silhouette-coefficient.streamlit.app
This website is designed to cluster various types of data using the K-Means and K-Medoids algorithms. Among the many clustering algorithms, K-Means and K-Medoids are closely related. K-Means is the most commonly used clustering algorithm due to its simplicity, ease of implementation, relative speed, and adaptability. It can handle large datasets efficiently. K-Medoids, also known as Partitioning Around Medoids (PAM), is an improvement on K-Means. It aims to reduce sensitivity to extreme values in the dataset. K-Medoids addresses K-Means' weakness of being sensitive to outliers, as objects with large values may significantly deviate from the data distribution.
Both K-Means and K-Medoids have proven to be highly effective in clustering data, offering high accuracy and efficiency in processing large numbers of objects. However, the biggest challenge in using these algorithms is determining the optimal number of clusters (k) for the data being analyzed. The Silhouette Coefficient is an algorithm that can help overcome this challenge. It's designed to determine the optimal number of clusters by using the average silhouette value, which provides an objective measurement of cluster quality. Its ability to analyze the number of k clusters can be used to optimize the performance of K-Means and K-Medoids.
This website aims to cluster various types of data using the K-Means and K-Medoids algorithms. While both algorithms are effective and widely used for their simplicity and efficiency, they face a significant challenge: determining the optimal number of clusters (k) for the data, which can significantly impact the accuracy and usefulness of the clustering results.