{ Snipperize } /clustering

Snippets about clustering

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K-means clustering

Hierarchical clustering gives a nice tree as a result, but it has a couple of disadvan- tages. The tree view doesn’t really break the data into distinct groups without additional work, and the algorithm is extremely computationally intensive. Because the relationship between every pair of items must be calculated and then recalculated when items are merged, the algorithm will run slowly on very large datasets. An alternative method of clustering is K-means clustering. This type of algorithmis quite different from hierarchical clustering because it is told in advance how many distinct clusters to generate. The algorithm will determine the size of the clusters based on the structure of the data. K-means clustering begins with k randomly placed centroids (points in space that represent the center of the cluster), and assigns every item to the nearest one. After the assignment, the centroids are moved to the average location of all the nodes assigned to them, and the assignments are redone. This process repeats until the assignments stop changing.

Python / clustering, K-means, algorithm / by ThePeppersStudio (440 days, 6.52 hours ago)