Tag: unsupervised learning

Data Science, Machine Learning

Hierarchical Clustering

RendyK

Hierarchical clustering is an unsupervised machine learning that identify closest cluster and group them together. Basic of Machine Learning article can be found here. Hierarchical clustering  works with only 2 steps repeatedly. Firstly, detect 2 or more closest points or clusters. Secondly, group them together. The next steps are the iteration of the first two steps until all of the data points are grouped in clusters. The illustration below describes how hierarchical clustering groups data points and build dendrogram at the same time.

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Data Science, Machine Learning

K-means

RendyK

k-means is a unsupervised Machine Learning to perform cluster analysis. More discussion about Machine Learning can be found here. Cluster analysis is meant to divide a group of data points into clusters according to their similarity pattern. k-means locates a number of k centroids among the data points to which each data point is clustered to the nearest centroid. The location of the k or centroid represents the center of their clusters.  The distance of centroid to each data point in the cluster is computed to be as small as possible. The number of k is the number of centroids or clusters that the data points will be distinguished into.

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