K-Means Clustering Who is my neighbor?.

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Presentation transcript:

K-Means Clustering Who is my neighbor?

Copyright GA Tagliarini, PhD K-Means Algorithm Initialize Acquire data Select number of clusters Create cluster center starting locations Make initial cluster assignments Until (time runs out or assignments stop changing or means become fixed) Update cluster center locations Update cluster assignments 12/8/2018 Copyright GA Tagliarini, PhD

Copyright GA Tagliarini, PhD Data 12/8/2018 Copyright GA Tagliarini, PhD

Data with initial cluster centers 12/8/2018 Copyright GA Tagliarini, PhD

Add the Final Cluster Centers 12/8/2018 Copyright GA Tagliarini, PhD

Relative Merits of k-Means Clustering Minuses The number of clusters must be selected first Oscillations are possible Plusses Adaptive Converges relatively quickly in practice Simple to implement 12/8/2018 Copyright GA Tagliarini, PhD

Copyright GA Tagliarini, PhD Testing Code 12/8/2018 Copyright GA Tagliarini, PhD