Perform k-means clustering in preparation for a KMKNN nearest-neighbors search.
A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions).
Logical scalar indicating whether
String specifying the type of distance to use.
Further arguments to pass to
This function is automatically called by
findKmknn and related functions.
However, it can be called directly by the user to save time if multiple queries are to be performed to the same
A KmknnIndex object containing:
data, a numeric matrix with points in the columns and dimensions in the rows, i.e., transposed relative to the input.
Points have also been reordered to improve data locality during the nearest-neighbor search.
Specifically, points in the same cluster are contiguous and ordered by increasing distance from the cluster center.
clusters, itself a list containing:
centers, a numeric matrix of cluster center coordinates where each column corresponds to a cluster.
info, another list of length equal to the number of clusters.
Each entry corresponds to a column of
centers (let's say cluster j) and is a list of length 2.
The first element is the zero-index of the first cell in the output
X that is assigned to j.
The second element is the distance of each point in the cluster from the cluster center.
order, an integer vector specifying how rows in
X have been reordered in columns of
NAMES, a character vector or
NULL equal to
distance, a string specifying the distance metric used.
kmeans for optional arguments.
KmknnIndex for details on the output class.
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