# K-Means¶

The K-Means algorithm solves clustering problem by partitioning $$n$$ feature vectors into $$k$$ clusters minimizing some criterion. Each cluster is characterized by a representative point, called a centroid.

 Operation Computational methods Programming Interface Training Lloyd’s train(…) train_input train_result Inference Lloyd’s infer(…) infer_input infer_result

## Mathematical formulation¶

### Training¶

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 centroids that minimize the objective function

$\Phi_{X}(C) = \sum_{i = 1}^n d^2(x_i, C),$

where $$d^2(x_i, C)$$ is the squared Euclidean distance from $$x_i$$ to the closest centroid in $$C$$,

$d^2(x_i, C) = \min_{1 \leq j \leq k} \| x_i - c_j \|^2, \quad 1 \leq i \leq n.$

Expression $$\|\cdot\|$$ denotes $$L_2$$ norm.

Note

In the general case, $$d$$ may be an arbitrary distance function. Current version of the oneDAL spec defines only Euclidean distance case.

#### Training method: Lloyd’s¶

The Lloyd’s method [Lloyd82] consists in iterative updates of centroids by applying the alternating Assignment and Update steps, where $$t$$ denotes a index of the current iteration, e.g., $$C^{(t)} = \{ c_1^{(t)}, \ldots, c_k^{(t)} \}$$ is the set of centroids at the $$t$$-th iteration. The method requires the initial centroids $$C^{(1)}$$ to be specified at the beginning of the algorithm ($$t = 1$$).

(1) Assignment step: Assign each feature vector $$x_i$$ to the nearest centroid. $$y_i^{(t)}$$ denotes the assigned label (cluster index) to the feature vector $$x_i$$.

$y_i^{(t)} = \mathrm{arg}\min_{1 \leq j \leq k} \| x_i - c_j^{(t)} \|^2, \quad 1 \leq i \leq n.$

Each feature vector from the training set $$X$$ is assigned to exactly one centroid so that $$X$$ is partitioned to $$k$$ disjoint sets (clusters)

$S_j^{(t)} = \big\{ \; x_i \in X : \; y_i^{(t)} = j \; \big\}, \quad 1 \leq j \leq k.$

(2) Update step: Recalculate centroids by averaging feature vectors assigned to each cluster.

$c_j^{(t + 1)} = \frac{1}{|S_j^{(t)}|} \sum_{x \in S_j^{(t)}} x, \quad 1 \leq j \leq k.$

The steps (1) and (2) are performed until the following stop condition,

$\sum_{j=1}^k \big\| c_j^{(t)} - c_j^{(t+1)} \big\|^2 < \varepsilon,$

is satisfied or number of iterations exceeds the maximal value $$T$$ defined by the user.

### Inference¶

Given the inference set $$X' = \{ x_1', \ldots, x_m' \}$$ of $$p$$-dimensional feature vectors and the set $$C = \{ c_1, \ldots, c_k \}$$ of centroids produced at the training stage, the problem is to predict the index $$y_j' \in \{ 0, \ldots, k-1 \}$$, $$1 \leq j \leq m$$, of the centroid in accordance with a method-defined rule.

#### Inference method: Lloyd’s¶

Lloyd’s inference method computes the $$y_j'$$ as an index of the centroid closest to the feature vector $$x_j'$$,

$y_j' = \mathrm{arg}\min_{1 \leq l \leq k} \| x_j' - c_l \|^2, \quad 1 \leq j \leq m.$

## Usage example¶

### Training¶

kmeans::model<> run_training(const table& data,
const table& initial_centroids) {
const auto kmeans_desc = kmeans::descriptor<float>{}
.set_cluster_count(10)
.set_max_iteration_count(50)
.set_accuracy_threshold(1e-4);

const auto result = train(kmeans_desc, data, initial_centroids);

print_table("labels", result.get_labels());
print_table("centroids", result.get_model().get_centroids());
print_value("objective", result.get_objective_function_value());

return result.get_model();
}

### Inference¶

table run_inference(const kmeans::model<>& model,
const table& new_data) {
const auto kmeans_desc = kmeans::descriptor<float>{}
.set_cluster_count(model.get_cluster_count());

const auto result = infer(kmeans_desc, model, new_data);

print_table("labels", result.get_labels());
}

## Examples¶

Batch Processing:

Batch Processing:

Batch Processing:

## Programming Interface¶

All types and functions in this section are declared in the oneapi::dal::kmeans namespace and be available via inclusion of the oneapi/dal/algo/kmeans.hpp header file.

### Descriptor¶

template<typename Float = detail::descriptor_base<>::float_t, typename Method = detail::descriptor_base<>::method_t, typename Task = detail::descriptor_base<>::task_t>
class descriptor
Template Parameters
• Float – The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

• Method – Tag-type that specifies an implementation of algorithm. Can be method::v1::lloyd_dense.

• Task – Tag-type that specifies the type of the problem to solve. Can be task::v1::clustering.

Constructors

descriptor(std::int64_t cluster_count = 2)

Creates a new instance of the class with the given cluster_count.

Public Methods

auto &set_cluster_count(int64_t value)
auto &set_max_iteration_count(int64_t value)
auto &set_accuracy_threshold(double value)

#### Method tags¶

struct lloyd_dense

Tag-type that denotes Lloyd’s computational method.

using by_default = lloyd_dense

Alias tag-type for Lloyd’s computational method.

struct clustering

Tag-type that parameterizes entities used for solving clustering problem.

using by_default = clustering

Alias tag-type for the clustering task.

### Model¶

class model
Template Parameters

Task – Tag-type that specifies type of the problem to solve. Can be task::v1::clustering.

Constructors

model()

Creates a new instance of the class with the default property values.

Properties

const table &centroids = table{}

A $$k \times p$$ table with the cluster centroids. Each row of the table stores one centroid.

Getter & Setter
const table & get_centroids() const
auto & set_centroids(const table &value)
std::int64_t cluster_count = 0

Number of clusters k in the trained model.

Getter & Setter
std::int64_t get_cluster_count() const
Invariants

### Training train(...)¶

#### Input¶

class train_input
Template Parameters

Task – Tag-type that specifies type of the problem to solve. Can be task::v1::clustering.

Constructors

train_input(const table &data)
train_input(const table &data, const table &initial_centroids)

Creates a new instance of the class with the given data and initial_centroids.

Properties

const table &data

An $$n \times p$$ table with the data to be clustered, where each row stores one feature vector.

Getter & Setter
const table & get_data() const
auto & set_data(const table &data)
const table &initial_centroids

A $$k \times p$$ table with the initial centroids, where each row stores one centroid.

Getter & Setter
const table & get_initial_centroids() const
auto & set_initial_centroids(const table &data)

#### Result¶

class train_result
Template Parameters

Task – Tag-type that specifies type of the problem to solve. Can be task::v1::clustering.

Constructors

train_result()

Creates a new instance of the class with the default property values.

Properties

The trained K-means model.

Getter & Setter
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
const table &labels = table{}

An $$n \times 1$$ table with the labels $$y_i$$ assigned to the samples $$x_i$$ in the input data, $$1 \leq 1 \leq n$$.

Getter & Setter
const table & get_labels() const
auto & set_labels(const table &value)
int64_t iteration_count = 0

The number of iterations performed by the algorithm.

Getter & Setter
int64_t get_iteration_count() const
auto & set_iteration_count(std::int64_t value)
Invariants
double objective_function_value

The value of the objective function $$\Phi_X(C)$$, where C is model.centroids (see kmeans::v1::model::centroids).

Getter & Setter
double get_objective_function_value() const
auto & set_objective_function_value(double value)
Invariants

#### Operation¶

template<typename Descriptor>
kmeans::train_result train(const Descriptor &desc, const kmeans::train_input &input)
Template Parameters
• desc – K-Means algorithm descriptor kmeans::desc

• input – Input data for the training operation

Preconditions
input.data.has_data == true
input.initial_centroids.row_count == desc.cluster_count
input.initial_centroids.column_count == input.data.column_count
Postconditions
result.labels.row_count == input.data.row_count
result.labels.column_count == 1
result.labels[i] >= 0
result.labels[i] < desc.cluster_count
result.iteration_count <= desc.max_iteration_count
result.model.centroids.row_count == desc.cluster_count
result.model.centroids.column_count == input.data.column_count

### Inference infer(...)¶

#### Input¶

class infer_input
Template Parameters

Task – Tag-type that specifies type of the problem to solve. Can be task::v1::clustering.

Constructors

infer_input(const model<Task> &trained_model, const table &data)

Creates a new instance of the class with the given model and data.

Properties

An $$n \times p$$ table with the data to be assigned to the clusters, where each row stores one feature vector.

Getter & Setter
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
const table &data = table{}

The trained K-Means model.

Getter & Setter
const table & get_data() const
auto & set_data(const table &value)

#### Result¶

class infer_result
Template Parameters

Task – Tag-type that specifies type of the problem to solve. Can be task::v1::clustering.

Constructors

infer_result()

Creates a new instance of the class with the default property values.

Properties

const table &labels = table{}

An $$n \times 1$$ table with assignments labels to feature vectors in the input data.

Getter & Setter
const table & get_labels() const
auto & set_labels(const table &value)
double objective_function_value = 0.0

The value of the objective function $$\Phi_X(C)$$, where C is defined by the corresponding infer_input::model::centroids.

Getter & Setter
double get_objective_function_value() const
auto & set_objective_function_value(double value)
Invariants

#### Operation¶

template<typename Descriptor>
kmeans::infer_result infer(const Descriptor &desc, const kmeans::infer_input &input)
Template Parameters
• desc – K-Means algorithm descriptor kmeans::desc

• input – Input data for the inference operation

Preconditions
input.data.has_data == true
input.model.centroids.has_data == true
input.model.centroids.row_count == desc.cluster_count
input.model.centroids.column_count == input.data.column_count
Postconditions
result.labels.row_count == input.data.row_count
result.labels.column_count == 1
result.labels[i] >= 0
result.labels[i] < desc.cluster_count