kmeans
kmeans clustering
Syntax
Description
performs kmeans
clustering to partition the observations of the
nbyp data matrix idx
= kmeans(X
,k
)X
into k
clusters, and returns an nby1 vector
(idx
) containing cluster indices of each observation. Rows of
X
correspond to points and columns correspond to variables.
By default, kmeans
uses the squared Euclidean distance metric and the kmeans++
algorithm for cluster center initialization.
returns
the cluster indices with additional options specified by one or more idx
= kmeans(X
,k
,Name,Value
)Name,Value
pair
arguments.
For example, specify the cosine distance, the number of times to repeat the clustering using new initial values, or to use parallel computing.
Examples
Train a kMeans Clustering Algorithm
Cluster data using kmeans clustering, then plot the cluster regions.
Load Fisher's iris data set. Use the petal lengths and widths as predictors.
load fisheriris X = meas(:,3:4); figure; plot(X(:,1),X(:,2),'k*','MarkerSize',5); title 'Fisher''s Iris Data'; xlabel 'Petal Lengths (cm)'; ylabel 'Petal Widths (cm)';
The larger cluster seems to be split into a lower variance region and a higher variance region. This might indicate that the larger cluster is two, overlapping clusters.
Cluster the data. Specify k = 3 clusters.
rng(1); % For reproducibility
[idx,C] = kmeans(X,3);
idx
is a vector of predicted cluster indices corresponding to the observations in X
. C
is a 3by2 matrix containing the final centroid locations.
Use kmeans
to compute the distance from each centroid to points on a grid. To do this, pass the centroids (C
) and points on a grid to kmeans
, and implement one iteration of the algorithm.
x1 = min(X(:,1)):0.01:max(X(:,1)); x2 = min(X(:,2)):0.01:max(X(:,2)); [x1G,x2G] = meshgrid(x1,x2); XGrid = [x1G(:),x2G(:)]; % Defines a fine grid on the plot idx2Region = kmeans(XGrid,3,'MaxIter',1,'Start',C);
Warning: Failed to converge in 1 iterations.
% Assigns each node in the grid to the closest centroid
kmeans
displays a warning stating that the algorithm did not converge, which you should expect since the software only implemented one iteration.
Plot the cluster regions.
figure; gscatter(XGrid(:,1),XGrid(:,2),idx2Region,... [0,0.75,0.75;0.75,0,0.75;0.75,0.75,0],'..'); hold on; plot(X(:,1),X(:,2),'k*','MarkerSize',5); title 'Fisher''s Iris Data'; xlabel 'Petal Lengths (cm)'; ylabel 'Petal Widths (cm)'; legend('Region 1','Region 2','Region 3','Data','Location','SouthEast'); hold off;
Partition Data into Two Clusters
Randomly generate the sample data.
rng default; % For reproducibility X = [randn(100,2)*0.75+ones(100,2); randn(100,2)*0.5ones(100,2)]; figure; plot(X(:,1),X(:,2),'.'); title 'Randomly Generated Data';
There appears to be two clusters in the data.
Partition the data into two clusters, and choose the best arrangement out of five initializations. Display the final output.
opts = statset('Display','final'); [idx,C] = kmeans(X,2,'Distance','cityblock',... 'Replicates',5,'Options',opts);
Replicate 1, 3 iterations, total sum of distances = 201.533. Replicate 2, 5 iterations, total sum of distances = 201.533. Replicate 3, 3 iterations, total sum of distances = 201.533. Replicate 4, 3 iterations, total sum of distances = 201.533. Replicate 5, 2 iterations, total sum of distances = 201.533. Best total sum of distances = 201.533
By default, the software initializes the replicates separately using kmeans++.
Plot the clusters and the cluster centroids.
figure; plot(X(idx==1,1),X(idx==1,2),'r.','MarkerSize',12) hold on plot(X(idx==2,1),X(idx==2,2),'b.','MarkerSize',12) plot(C(:,1),C(:,2),'kx',... 'MarkerSize',15,'LineWidth',3) legend('Cluster 1','Cluster 2','Centroids',... 'Location','NW') title 'Cluster Assignments and Centroids' hold off
You can determine how well separated the clusters are by passing idx
to silhouette
.
Cluster Data Using Parallel Computing
Clustering large data sets might take time, particularly if you use online updates (set by default). If you have a Parallel Computing Toolbox ™ license and you set the options for parallel computing, then kmeans
runs each clustering task (or replicate) in parallel. And, if Replicates
>1, then parallel computing decreases time to convergence.
Randomly generate a large data set from a Gaussian mixture model.
rng(1); % For reproducibility Mu = ones(20,30).*(1:20)'; % Gaussian mixture mean rn30 = randn(30,30); Sigma = rn30'*rn30; % Symmetric and positivedefinite covariance Mdl = gmdistribution(Mu,Sigma); % Define the Gaussian mixture distribution X = random(Mdl,10000);
Mdl
is a 30dimensional gmdistribution
model with 20 components. X
is a 10000by30 matrix of data generated from Mdl
.
Specify the options for parallel computing.
stream = RandStream('mlfg6331_64'); % Random number stream options = statset('UseParallel',1,'UseSubstreams',1,... 'Streams',stream);
The input argument 'mlfg6331_64'
of RandStream
specifies to use the multiplicative lagged Fibonacci generator algorithm. options
is a structure array with fields that specify options for controlling estimation.
Cluster the data using kmeans clustering. Specify that there are k = 20 clusters in the data and increase the number of iterations. Typically, the objective function contains local minima. Specify 10 replicates to help find a lower, local minimum.
tic; % Start stopwatch timer [idx,C,sumd,D] = kmeans(X,20,'Options',options,'MaxIter',10000,... 'Display','final','Replicates',10);
Starting parallel pool (parpool) using the 'Processes' profile ... Connected to the parallel pool (number of workers: 6). Replicate 4, 79 iterations, total sum of distances = 7.62412e+06. Replicate 2, 56 iterations, total sum of distances = 7.62036e+06. Replicate 3, 76 iterations, total sum of distances = 7.62583e+06. Replicate 6, 96 iterations, total sum of distances = 7.6258e+06. Replicate 5, 103 iterations, total sum of distances = 7.61753e+06. Replicate 1, 94 iterations, total sum of distances = 7.60746e+06. Replicate 10, 66 iterations, total sum of distances = 7.62582e+06. Replicate 8, 113 iterations, total sum of distances = 7.60741e+06. Replicate 9, 80 iterations, total sum of distances = 7.60592e+06. Replicate 7, 77 iterations, total sum of distances = 7.61939e+06. Best total sum of distances = 7.60592e+06
toc % Terminate stopwatch timer
Elapsed time is 72.873647 seconds.
The Command Window indicates that six workers are available. The number of workers might vary on your system. The Command Window displays the number of iterations and the terminal objective function value for each replicate. The output arguments contain the results of replicate 9 because it has the lowest total sum of distances.
Assign New Data to Existing Clusters and Generate C/C++ Code
kmeans
performs kmeans clustering to partition data into k clusters. When you have a new data set to cluster, you can create new clusters that include the existing data and the new data by using kmeans
. The kmeans
function supports C/C++ code generation, so you can generate code that accepts training data and returns clustering results, and then deploy the code to a device. In this workflow, you must pass training data, which can be of considerable size. To save memory on the device, you can separate training and prediction by using kmeans
and pdist2
, respectively.
Use kmeans
to create clusters in MATLAB® and use pdist2
in the generated code to assign new data to existing clusters. For code generation, define an entrypoint function that accepts the cluster centroid positions and the new data set, and returns the index of the nearest cluster. Then, generate code for the entrypoint function.
Generating C/C++ code requires MATLAB® Coder™.
Perform kMeans Clustering
Generate a training data set using three distributions.
rng('default') % For reproducibility X = [randn(100,2)*0.75+ones(100,2); randn(100,2)*0.5ones(100,2); randn(100,2)*0.75];
Partition the training data into three clusters by using kmeans
.
[idx,C] = kmeans(X,3);
Plot the clusters and the cluster centroids.
figure gscatter(X(:,1),X(:,2),idx,'bgm') hold on plot(C(:,1),C(:,2),'kx') legend('Cluster 1','Cluster 2','Cluster 3','Cluster Centroid')
Assign New Data to Existing Clusters
Generate a test data set.
Xtest = [randn(10,2)*0.75+ones(10,2); randn(10,2)*0.5ones(10,2); randn(10,2)*0.75];
Classify the test data set using the existing clusters. Find the nearest centroid from each test data point by using pdist2
.
[~,idx_test] = pdist2(C,Xtest,'euclidean','Smallest',1);
Plot the test data and label the test data using idx_test
by using gscatter
.
gscatter(Xtest(:,1),Xtest(:,2),idx_test,'bgm','ooo') legend('Cluster 1','Cluster 2','Cluster 3','Cluster Centroid', ... 'Data classified to Cluster 1','Data classified to Cluster 2', ... 'Data classified to Cluster 3')
Generate Code
Generate C code that assigns new data to the existing clusters. Note that generating C/C++ code requires MATLAB® Coder™.
Define an entrypoint function named findNearestCentroid
that accepts centroid positions and new data, and then find the nearest cluster by using pdist2
.
Add the %#codegen
compiler directive (or pragma) to the entrypoint function after the function signature to indicate that you intend to generate code for the MATLAB algorithm. Adding this directive instructs the MATLAB Code Analyzer to help you diagnose and fix violations that would cause errors during code generation.
type findNearestCentroid % Display contents of findNearestCentroid.m
function idx = findNearestCentroid(C,X) %#codegen [~,idx] = pdist2(C,X,'euclidean','Smallest',1); % Find the nearest centroid
Note: If you click the button located in the upperright section of this page and open this example in MATLAB®, then MATLAB® opens the example folder. This folder includes the entrypoint function file.
Generate code by using codegen
(MATLAB Coder). Because C and C++ are statically typed languages, you must determine the properties of all variables in the entrypoint function at compile time. To specify the data type and array size of the inputs of findNearestCentroid
, pass a MATLAB expression that represents the set of values with a certain data type and array size by using the args
option. For details, see Specify VariableSize Arguments for Code Generation.
codegen findNearestCentroid args {C,Xtest}
Code generation successful.
codegen
generates the MEX function findNearestCentroid_mex
with a platformdependent extension.
Verify the generated code.
myIndx = findNearestCentroid(C,Xtest); myIndex_mex = findNearestCentroid_mex(C,Xtest); verifyMEX = isequal(idx_test,myIndx,myIndex_mex)
verifyMEX = logical
1
isequal
returns logical 1 (true
), which means all the inputs are equal. The comparison confirms that the pdist2
function, the findNearestCentroid
function, and the MEX function return the same index.
You can also generate optimized CUDA® code using GPU Coder™.
cfg = coder.gpuConfig('mex'); codegen config cfg findNearestCentroid args {C,Xtest}
For more information on code generation, see General Code Generation Workflow. For more information on GPU coder, see Get Started with GPU Coder (GPU Coder) and Supported Functions (GPU Coder).
Input Arguments
X
— Data
numeric matrix
Data, specified as a numeric matrix. The rows of X
correspond
to observations, and the columns correspond to variables.
If X
is a numeric vector, then kmeans
treats
it as an nby1 data matrix, regardless of its
orientation.
The software treats NaN
s in X
as
missing data and removes any row of X
that contains at
least one NaN
. Removing rows of X
reduces the sample size. The kmeans
function
returns NaN
for the corresponding value in the output
argument idx
.
Data Types: single
 double
k
— Number of clusters
positive integer
Number of clusters in the data, specified as a positive integer.
Data Types: single
 double
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: 'Distance','cosine','Replicates',10,'Options',statset('UseParallel',1)
specifies
the cosine distance, 10
replicate clusters at different
starting values, and to use parallel computing.
Display
— Level of output to display
'off'
(default)  'final'
 'iter'
Level of output to display in the Command Window, specified
as the commaseparated pair consisting of 'Display'
and
one of the following options:
'final'
— Displays results of the final iteration'iter'
— Displays results of each iteration'off'
— Displays nothing
Example: 'Display','final'
Distance
— Distance metric
'sqeuclidean'
(default)  'cityblock'
 'cosine'
 'correlation'
 'hamming'
Distance metric, in p
dimensional space, used for
minimization, specified as the commaseparated pair consisting of
'Distance'
and 'sqeuclidean'
,
'cityblock'
, 'cosine'
,
'correlation'
, or
'hamming'
.
kmeans
computes centroid clusters differently for
the supported distance metrics. This table summarizes the available
distance metrics. In the formulae, x is an
observation (that is, a row of X
) and
c is a centroid (a row vector).
Distance Metric  Description  Formula 

'sqeuclidean'  Squared Euclidean distance (default). Each centroid is the mean of the points in that cluster. 
$$d(x,c)=(xc)(xc{)}^{\prime}$$

'cityblock'  Sum of absolute differences, i.e., the L1 distance. Each centroid is the componentwise median of the points in that cluster. 
$$d(x,c)={\displaystyle \sum _{j=1}^{p}\left{x}_{j}{c}_{j}\right}$$

'cosine'  One minus the cosine of the included angle between points (treated as vectors). Each centroid is the mean of the points in that cluster, after normalizing those points to unit Euclidean length. 
$$d(x,c)=1\frac{xc\prime}{\sqrt{\left(x{x}^{\prime}\right)\left(cc\prime \right)}}$$

'correlation'  One minus the sample correlation between points (treated as sequences of values). Each centroid is the componentwise mean of the points in that cluster, after centering and normalizing those points to zero mean and unit standard deviation. 
$$d(x,c)=1\frac{\left(x\overrightarrow{\overline{x}}\right){\left(c\overrightarrow{\overline{c}}\right)}^{\prime}}{\sqrt{\left(x\overrightarrow{\overline{x}}\right){\left(x\overrightarrow{\overline{x}}\right)}^{\prime}}\sqrt{\left(c\overrightarrow{\overline{c}}\right){\left(c\overrightarrow{\overline{c}}\right)}^{\prime}}},$$ where

'hamming'  This metric is only suitable for binary data. It is the proportion of bits that differ. Each centroid is the componentwise median of points in that cluster. 
$$d(x,y)=\frac{1}{p}{\displaystyle \sum}_{j=1}^{p}I\left\{{x}_{j}\ne {y}_{j}\right\},$$ where I is the indicator function. 
Example: 'Distance','cityblock'
EmptyAction
— Action to take if cluster loses all member observations
'singleton'
(default)  'error'
 'drop'
Action to take if a cluster loses all its member observations,
specified as the commaseparated pair consisting of 'EmptyAction'
and
one of the following options.
Value  Description 

'error'  Treat an empty cluster as an error. 
'drop'  Remove any clusters that become empty. 
'singleton'  Create a new cluster consisting of the one point furthest from its centroid (default). 
Example: 'EmptyAction','error'
MaxIter
— Maximum number of iterations
100
(default)  positive integer
Maximum number of iterations, specified as the commaseparated
pair consisting of 'MaxIter'
and a positive integer.
Example: 'MaxIter',1000
Data Types: double
 single
OnlinePhase
— Online update flag
'off'
(default)  'on'
Online update flag, specified as the commaseparated pair consisting
of 'OnlinePhase'
and 'off'
or 'on'
.
If OnlinePhase
is on
,
then kmeans
performs an online update phase in
addition to a batch update phase. The online phase can be time consuming
for large data sets, but guarantees a solution that is a local minimum
of the distance criterion. In other words, the software finds a partition
of the data in which moving any single point to a different cluster
increases the total sum of distances.
Example: 'OnlinePhase','on'
Options
— Options for controlling iterative algorithm for minimizing fitting criteria
[]
(default)  structure array returned by statset
Options for controlling the iterative algorithm for minimizing the fitting criteria, specified
as the commaseparated pair consisting of 'Options'
and a structure array returned by statset
. Supported fields
of the structure array specify options for controlling the iterative
algorithm.
This table summarizes the supported fields. Note that the supported fields require Parallel Computing Toolbox™.
Field  Description 

'Streams'  A
In this case, use a
cell array the same size as the parallel pool. If
a parallel pool is not open, then

'UseParallel' 

'UseSubstreams'  Set to true to compute in a
reproducible fashion. The default is
false . To compute reproducibly,
set Streams to a type allowing
substreams: 'mlfg6331_64' or
'mrg32k3a' . 
To ensure more predictable results, use parpool
(Parallel Computing Toolbox) and explicitly
create a parallel pool before invoking kmeans
and
setting 'Options',statset('UseParallel',1)
.
Example: 'Options',statset('UseParallel',1)
Data Types: struct
Replicates
— Number of times to repeat clustering using new initial cluster centroid positions
1
(default)  positive integer
Number of times to repeat clustering using new initial cluster
centroid positions, specified as the commaseparated pair consisting
of 'Replicates'
and an integer. kmeans
returns
the solution with the lowest sumd
.
You can set 'Replicates'
implicitly by supplying
a 3D array as the value for the 'Start'
namevalue
pair argument.
Example: 'Replicates',5
Data Types: double
 single
Start
— Method for choosing initial cluster centroid positions
'plus'
(default)  'cluster'
 'sample'
 'uniform'
 numeric matrix  numeric array
Method for choosing initial cluster centroid positions (or seeds),
specified as the commaseparated pair consisting of
'Start'
and 'cluster'
,
'plus'
, 'sample'
,
'uniform'
, a numeric matrix, or a numeric array.
This table summarizes the available options for choosing
seeds.
Value  Description 

'cluster'  Perform a preliminary clustering phase on a
random 10% subsample of If the
number of observations in the random 10% subsample
is less than 
'plus' (default)  Select k seeds by implementing
the kmeans++ algorithm
for cluster center initialization. 
'sample'  Select k observations from
X at random. 
'uniform'  Select k points uniformly at
random from the range of X . Not
valid with the Hamming distance. 
numeric matrix  k byp
matrix of centroid starting locations. The rows of
Start correspond to seeds. The
software infers k from the first
dimension of Start , so you can
pass in [] for
k . 
numeric array  k bypbyr
array of centroid starting locations. The rows of
each page correspond to seeds. The third dimension
invokes replication of the clustering routine. Page
j contains the set of seeds for
replicate j. The software infers
the number of replicates (specified by the
'Replicates' namevalue pair
argument) from the size of the third
dimension. 
Example: 'Start','sample'
Data Types: char
 string
 double
 single
Output Arguments
idx
— Cluster indices
numeric column vector
Cluster indices, returned as a numeric column vector. idx
has
as many rows as X
, and each row indicates the cluster
assignment of the corresponding observation.
sumd
— Withincluster sums of pointtocentroid distances
numeric column vector
Withincluster sums of pointtocentroid distances, returned as a numeric column vector.
sumd
is a k
by1 vector, where
element j is the sum of pointtocentroid distances
within cluster j. By default, kmeans
uses the squared Euclidean distance (see 'Distance'
metrics).
D
— Distances from each point to every centroid
numeric matrix
Distances from each point to every centroid, returned as a numeric matrix.
D
is an
nbyk
matrix, where element
(j,m) is the distance from
observation j to centroid m. By
default, kmeans
uses the squared Euclidean distance
(see 'Distance'
metrics).
More About
kMeans Clustering
kmeans clustering, or Lloyd’s algorithm [2], is an iterative, datapartitioning algorithm that assigns n observations to exactly one of k clusters defined by centroids, where k is chosen before the algorithm starts.
The algorithm proceeds as follows:
Choose k initial cluster centers (centroid). For example, choose k observations at random (by using
'Start','sample'
) or use the kmeans ++ algorithm for cluster center initialization (the default).Compute pointtoclustercentroid distances of all observations to each centroid.
There are two ways to proceed (specified by
OnlinePhase
):Batch update — Assign each observation to the cluster with the closest centroid.
Online update — Individually assign observations to a different centroid if the reassignment decreases the sum of the withincluster, sumofsquares pointtoclustercentroid distances.
For more details, see Algorithms.
Compute the average of the observations in each cluster to obtain k new centroid locations.
Repeat steps 2 through 4 until cluster assignments do not change, or the maximum number of iterations is reached.
kmeans++ Algorithm
The kmeans++ algorithm uses an heuristic to find centroid seeds for kmeans clustering. According to Arthur and Vassilvitskii [1], kmeans++ improves the running time of Lloyd’s algorithm, and the quality of the final solution.
The kmeans++ algorithm chooses seeds as follows, assuming the number of clusters is k.
Select an observation uniformly at random from the data set, X. The chosen observation is the first centroid, and is denoted c_{1}.
Compute distances from each observation to c_{1}. Denote the distance between c_{j} and the observation m as $$d\left({x}_{m},{c}_{j}\right)$$.
Select the next centroid, c_{2} at random from X with probability
$$\frac{{d}^{2}\left({x}_{m},{c}_{1}\right)}{{\displaystyle \sum}_{j=1}^{n}{d}^{2}\left({x}_{j},{c}_{1}\right)}.$$
To choose center j:
Compute the distances from each observation to each centroid, and assign each observation to its closest centroid.
For m = 1,...,n and p = 1,...,j – 1, select centroid j at random from X with probability
$$\frac{{d}^{2}\left({x}_{m},{c}_{p}\right)}{{\displaystyle \sum}_{\{h;{x}_{h}\in {C}_{p}\}}^{}{d}^{2}\left({x}_{h},{c}_{p}\right)},$$
where C_{p} is the set of all observations closest to centroid c_{p} and x_{m} belongs to C_{p}.
That is, select each subsequent center with a probability proportional to the distance from itself to the closest center that you already chose.
Repeat step 4 until k centroids are chosen.
Arthur and Vassilvitskii [1] demonstrate, using a simulation study for several cluster orientations, that kmeans++ achieves faster convergence to a lower sum of withincluster, sumofsquares pointtoclustercentroid distances than Lloyd’s algorithm.
Algorithms
kmeans
uses a twophase iterative algorithm to minimize the sum of pointtocentroid distances, summed over allk
clusters.This first phase uses batch updates, where each iteration consists of reassigning points to their nearest cluster centroid, all at once, followed by recalculation of cluster centroids. This phase occasionally does not converge to solution that is a local minimum. That is, a partition of the data where moving any single point to a different cluster increases the total sum of distances. This is more likely for small data sets. The batch phase is fast, but potentially only approximates a solution as a starting point for the second phase.
This second phase uses online updates, where points are individually reassigned if doing so reduces the sum of distances, and cluster centroids are recomputed after each reassignment. Each iteration during this phase consists of one pass though all the points. This phase converges to a local minimum, although there might be other local minima with lower total sum of distances. In general, finding the global minimum is solved by an exhaustive choice of starting points, but using several replicates with random starting points typically results in a solution that is a global minimum.
If
Replicates
= r > 1 andStart
isplus
(the default), then the software selects r possibly different sets of seeds according to the kmeans++ algorithm.If you enable the
UseParallel
option inOptions
andReplicates
> 1, then each worker selects seeds and clusters in parallel.
References
[1] Arthur, David, and Sergi Vassilvitskii. “Kmeans++: The Advantages of Careful Seeding.” SODA ‘07: Proceedings of the Eighteenth Annual ACMSIAM Symposium on Discrete Algorithms. 2007, pp. 1027–1035.
[2] Lloyd, Stuart P. “Least Squares Quantization in PCM.” IEEE Transactions on Information Theory. Vol. 28, 1982, pp. 129–137.
[3] Seber, G. A. F. Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc., 1984.
[4] Spath, H. Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples. Translated by J. Goldschmidt. New York: Halsted Press, 1985.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
Usage notes and limitations:
Supported syntaxes are:
idx = kmeans(X,k)
[idx,C] = kmeans(X,k)
[idx,C,sumd] = kmeans(X,k)
[___] = kmeans(___,Name,Value)
Supported namevalue pair arguments, and any differences, are:
'Display'
— Default value is'iter'
.'MaxIter'
'Options'
— Supports only the'TolFun'
field of the structure array created bystatset
. The default value of'TolFun'
is1e4
. Thekmeans
function uses the value of'TolFun'
as the termination tolerance for the withincluster sums of pointtocentroid distances. For example, you can specify'Options',statset('TolFun',1e8)
.'Replicates'
'Start'
— Supports only'plus'
,'sample'
, and a numeric array.
For more information, see Tall Arrays for OutofMemory Data.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
If the
Start
method uses random selections, the initial centroid cluster positions might not match MATLAB^{®}.If the number of rows in
X
is fixed, code generation does not remove rows ofX
that contain aNaN
.The cluster centroid locations in
C
can have a different order than in MATLAB. In this case, the cluster indices inidx
have corresponding differences.If you provide
Display
, its value must be'off'
.If you provide
Streams
, it must be empty andUseSubstreams
must befalse
.When you set the
UseParallel
option totrue
:Some computations can execute in parallel even when
Replicates
is1
. For large data sets, whenReplicates
is1
, consider setting theUseParallel
option totrue
.kmeans
usesparfor
(MATLAB Coder) to create loops that run in parallel on supported sharedmemory multicore platforms. Loops that run in parallel can be faster than loops that run on a single thread. If your compiler does not support the Open Multiprocessing (OpenMP) application interface or you disable OpenMP library, MATLAB Coder™ treats theparfor
loops asfor
loops. To find supported compilers, see Supported Compilers.
To save memory on the device to which you deploy generated code, you can separate training and prediction by using
kmeans
andpdist2
, respectively. Usekmeans
to create clusters in MATLAB and usepdist2
in the generated code to assign new data to existing clusters. For code generation, define an entrypoint function that accepts the cluster centroid positions and the new data set, and returns the index of the nearest cluster. Then, generate code for the entrypoint function. For an example, see Assign New Data to Existing Clusters and Generate C/C++ Code.kmeans
returns integertype (int32
) indices in generated standalone C/C++ code. Therefore, the function allows for stricter singleprecision support when you use singleprecision inputs. For MEX code generation, the function still returns doubleprecision indices to match the MATLAB behavior.Before R2020a:
kmeans
returns doubleprecision indices in generated standalone C/C++ code.
For more information on code generation, see Introduction to Code Generation and General Code Generation Workflow.
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, specify the Options
namevalue argument in the call to
this function and set the UseParallel
field of the
options structure to true
using
statset
:
Options=statset(UseParallel=true)
For more information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced before R2006a
See Also
linkage
 clusterdata
 silhouette
 parpool
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