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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
Computing	Dense	compute(…)	compute_input	compute_result

Mathematical formulation¶

Computing¶

Given the training set \(X = \{ x_1, \ldots, x_n \}\) of \(p\)-dimensional feature vectors and a positive integer \(k\), the problem is to find a set \(C = \{ c_1, \ldots, c_k \}\) of \(p\)-dimensional initial centroids.

Computing method: dense¶

The method chooses first \(k\) feature vectors from the training set \(X\).

Programming Interface¶

Refer to API Reference: K-Means initialization.

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¶

Batch Processing:

kmeans_init_dense.cpp

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