josh.meanings.classify
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.
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.
assignmentsclj
(assignments centroids distance-fn points)Returns the assignments of the points to the centroids.
Returns the assignments of the points to the centroids.
classifyclj
(classify centroids distance-fn point)Returns the index of the centroid that is closest to the point.
Returns the index of the centroid that is closest to the point.
dataset-assignmentsclj
(dataset-assignments centroids distance-fn cols points)Updates the assignments dataset with the new assignments.
Updates the assignments dataset with the new assignments.
dataset-assignments-seqclj
(dataset-assignments-seq centroids distance-fn cols points-seq)Updates a sequence of assignment datasets with the new assignments.
Updates a sequence of assignment datasets with the new assignments.
distancesclj
(distances centroids distance-fn point)Returns a vector of distance of the centroids from the point.
Returns a vector of distance of the centroids from the point.
max-indexclj
(max-index coll)Returns the index of the minimum value in a collection.
Returns the index of the minimum value in a collection.
min-indexclj
(min-index coll)Returns the index of the minimum value in a collection.
Returns the index of the minimum value in a collection.
sumclj
(sum coll)Returns the sum of the numbers in the sequence.
Returns the sum of the numbers in the sequence.
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