josh.meanings.kmeans — org.clojars.joshua/josh.meanings 0.3.5

K-Means clustering generates a specific number of disjoint, non-hierarchical clusters. It is well suited to generating globular clusters. The K-Means method is numerical, unsupervised, non-deterministic and iterative. Every member of a cluster is closer to its cluster center than the center of any other cluster.

The choice of initial partition can greatly affect the final clusters that result, in terms of inter-cluster and intracluster distances and cohesion. As a result k means is best run multiple times in order to avoid the trap of a local minimum.

assignments clj

(assignments centroids distance-fn points)

Returns the assignments of the points to the centroids.

calculate-objective clj

(calculate-objective s)

classify clj

(classify centroids distance-fn point)

Returns the index of the centroid that is closest to the point.

cost clj

(cost centroids distance-fn assignment point)

Returns the distance of an assigned point from its centroid.

costs clj

(costs centroids distance-fn assignments points)

Returns the distances of the points from their centroids.

dataset-assignments clj

(dataset-assignments centroids distance-fn cols points)

Updates the assignments dataset with the new assignments.

dataset-assignments-seq clj

(dataset-assignments-seq centroids distance-fn cols points-seq)

Updates a sequence of assignment datasets with the new assignments.

dataset-costs clj

(dataset-costs centroids distance-fn cols points)

Returns the distances of the points from their centroids.

dataset-costs-seq clj

(dataset-costs-seq centroids distance-fn cols points-seq)

Returns the distances of the points from their centroids.

default-distance-fn clj

default-format clj

default-init clj

default-run-count clj

distances clj

(distances centroids distance-fn point)

Returns a vector of distance of the centroids from the point.

estimate-size clj

(estimate-size config)

Update the number of records in the dataset.

initialize-centroids! clj

(initialize-centroids! s)

Calls initialize-centroids and writes the returned dataset to the centroids file.

initialize-k-means-state clj

(initialize-k-means-state points-file k options)

Sets initial configuration options for the k means calculation.

k-means clj multimethod

k-means-seq clj

(k-means-seq dataset k & options)

Returns a lazy sequence of m ClusterResult.

k-means-via-file clj

(k-means-via-file points-filepath k & options)

load-model clj

(load-model filename)

max-index clj

(max-index coll)

Returns the index of the minimum value in a collection.

min-index clj

(min-index coll)

Returns the index of the minimum value in a collection.

recalculate-means clj

(recalculate-means s)

regenerate-assignments! clj

(regenerate-assignments! s)

Writes the new assignments to the assignments file.

stabilized? clj

(stabilized? centroids-1 centroids-2)

K-means is said to be stabilized when performing an iterative refinement (often called a lloyd iteration), does not result in any shifting of points between clusters. A stabilized k-means calculation can be stopped, because further refinement won't produce any changes.

sum clj

(sum coll)

Returns the sum of the numbers in the sequence.

update-centroids clj

(update-centroids s)