Developer Guide and Reference

  • 2021.2
  • 03/26/2021
  • Public Content
Contents

K-Means initialization

The K-Means initialization algorithm receives
n
feature vectors as input and chooses
k
initial centroids. After initialization, K-Means algorithm uses the initialization result to partition input data into
k
clusters.
Operation
Computational methods
Programming Interface

Mathematical formulation

Computing
Given the training set LaTex Math image. of
p
-dimensional feature vectors and a positive integer
k
, the problem is to find a set LaTex Math image. of
p
-dimensional initial centroids.
Computing method:
dense
The method chooses first
k
feature vectors from the training set
X
.

Programming Interface

Usage example

Computing
table run_compute(const table& data) { const auto kmeans_desc = kmeans_init::descriptor<float, kmeans_init::method::dense>{} .set_cluster_count(10) const auto result = compute(kmeans_desc, data); print_table("centroids", result.get_centroids()); return result.get_centroids(); }

Examples

oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.