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Evaluasi dan Penerapan K-Means Clustering by Binar Ariamukti is a document available to read on EtoBox.

This document discusses clustering and its evaluation. It covers the following key points: 1. Clustering is an unsupervised learning technique that groups similar entities into clusters. Common clustering algorithms discussed are K-Means and DBSCAN. 2. A case study on determining meeting points for places of interest using driver location data is presented. Both DBSCAN and K-Means algorithms are applied, with K-Means requiring a predetermined number of clusters K. 3. The Davies-Bouldin index is introdu

Author

Binar Ariamukti

Language

EN