lambda-ml.clustering.k-means
K-means clustering.
Example usage:
(def data [[1 1] [1.5 2] [3 4] [5 7] [3.5 5] [4.5 5] [3.5 4.5]])
(let [k 2]
(-> (k-means k lambda-ml.distance/euclidean data)
(nth 100)))
;;=> {0 ([3.5 4.5] [4.5 5] [3.5 5] [5 7] [3 4]), 1 ([1.5 2] [1 1])}
assign-clusters
(assign-clusters f mu x)Returns cluster assignments based on the closest centroid to each point.
k-means
(k-means k f points)Returns a lazy sequence of a clustering of points using the distance function f, represented as a map from cluster id to a collection of points, at each iteration of k-means.
update-centroids
(update-centroids k clusters)Returns updated centroids based on the average of points in each cluster.