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What is K-Means Clustering: Euclidean vs Manhattan about?

This study compares Euclidean and Manhattan distances in K-Means clustering to optimize the grouping of palm oil production in North Sumatra, Indonesia. The research identifies that the optimal clustering is achieved with a Davies Bouldin Index (DBI) of 0.145 at k=2, categorizing Asahan, Langkat, and North Labuhanbatu as high production regions. The findings aim to enhance data mining techniques for better analysis of palm oil production data.

Author

natalie

Language

EN