Cluster analysis is the collective name given to a number of algorithms for grouping similar
objects into distinct categories. It is a form of exploratory data analysis aimed at grouping
observations in a way that minimizes the difference within groups while maximizing the difference
between groups.
In K-Means clustering, the number of clusters is fixed at the beginning.
A cluster is defined by its cluster center or centroid.
A number of initial cluster centers is chosen. The observations are assigned
to the closest cluster.
Each centroid is then recalculated as the mean of its members.
This changes the distances between cluster centers and observations,
so the observations are once again reassigned. This process is repeated
until no more observations change cluster.
Note that the final partition depends on the initial location of the centers.
Different applications may return different results for the same dataset.
Running a cluster analysis
K-Means clustering is implemented by the
KMeansClusterAnalysis class.
This class has three constructors. The first constructor takes one argument: a
MatrixT whose columns
contain the data to be analyzed.
The second constructor also takes one argument: an array of
VectorT objects.
Both these constructors are illustrated below:
var matrix = Matrix.CreateRandom(100, 10);
var kc1 = new KMeansClusterAnalysis(matrix, 3);
var vectors = matrix.Columns.ToArray();
var kc2 = new KMeansClusterAnalysis(vectors, 3);
Dim mat = Matrix.CreateRandom(100, 10)
Dim kc1 = New KMeansClusterAnalysis(mat, 3)
Dim vectors = mat.Columns.ToArray()
Dim kc2 = New KMeansClusterAnalysis(vectors, 3)
No code example is currently available or this language may not be supported.
let matrix = Matrix.CreateRandom(100, 10)
let kc1 = new KMeansClusterAnalysis(matrix, 3)
let vectors = matrix.Columns.ToArray()
let kc2 = new KMeansClusterAnalysis(vectors, 3)
The third constructor takes two arguments. The first is a
IDataFrame (a
DataFrameR, C or
MatrixT)
that contains the variables that may be used in the analysis.
The second argument is an array of strings
that contains the names of the variables from the collection that should be included
in the analysis.
var rowIndex = Index.Default(matrix.RowCount);
var names = new string[] { "x1", "x2", "x3",
"x4", "x5", "x6", "x7", "x8", "x9", "x10" };
var columnIndex = Index.Create(names);
var dataFrame = matrix.ToDataFrame(rowIndex, columnIndex);
var kc3 = new KMeansClusterAnalysis(dataFrame, names, 3);Dim rowIndex = Index.Default(mat.RowCount)
Dim names = {"x1", "x2", "x3",
"x4", "x5", "x6", "x7", "x8", "x9", "x10"}
Dim columnIndex = Index.Create(names)
Dim frame = mat.ToDataFrame(rowIndex, columnIndex)
Dim kc3 = New KMeansClusterAnalysis(frame, names, 3)No code example is currently available or this language may not be supported.
let rowIndex = Index.Default(matrix.RowCount)
let names = [| "x1"; "x2"; "x3";
"x4"; "x5"; "x6"; "x7"; "x8"; "x9"; "x10" |]
let columnIndex = Index.Create(names)
let dataFrame = matrix.ToDataFrame(rowIndex, columnIndex)
let kc3 = new KMeansClusterAnalysis(dataFrame, names, 3)
The outcome of the algorithm depends on this initialization. Different
methods have been devised to generate the best possible results.
Which method to use can be selected through the
InitializationMethod
property. It is of type
KMeansInitializationMethod
and can take on the following values:
Value | Description |
|---|
KMeansPlusPlus |
Use the K-means++ algorithm to compute initial centroids.
This is the default.
|
RandomCenters |
Use randomly selected observations as the centroid.
|
Forgy |
Same as RandomCenters.
Use randomly selected observations as the centroid.
|
RandomAssignments |
Assign each observation randomly to one of the clusters and uses the centroid
of each cluster.
|
The initialization procedure always involve some randomization. The
RandomNumberGenerator
property lets you set the random number generator that is used
to obtain any needed pseudo-random numbers.
Finally, the Standardize
property lets you specify whether variables should be standardized before running the analysis.
When variables are unequally scaled, some variables will make a larger contribution
to the distance than others. This can distort the clustering. To avoid this problem,
the variables can be standardized so they all contribute equally to the distance.
The default is to standardize variables.
The Compute method performs the actual calculations.
Once the computations are complete, a number of properties and methods give access to the results in detail.
The following code sample sets up some details of a K-means cluster analysis
and runs it:
kc1.InitializationMethod = KMeansInitializationMethod.KMeansPlusPlus;
kc1.RandomNumberGenerator = new MersenneTwister();
kc1.Standardize = true;
kc1.Fit();
kc1.InitializationMethod = KMeansInitializationMethod.KMeansPlusPlus
kc1.RandomNumberGenerator = New MersenneTwister()
kc1.Standardize = True
kc1.Fit()
No code example is currently available or this language may not be supported.
kc1.InitializationMethod <- KMeansInitializationMethod.KMeansPlusPlus
kc1.RandomNumberGenerator <- MersenneTwister()
kc1.Standardize <- true
kc1.Fit()
The Centers property
returns an array of vectors that contains the clusters centers.
The Predictions property
returns a CategoricalVectorT
that for each observation indicates the cluster to which it belongs.
The GetDistancesToCenters method returns a
VectorT that for each observation indicates the distance of the observation
from the center of its cluster.
The Clusters
property returns an array of KMeansCluster
objects that describes each cluster in detail.
The Center property returns the
center of the cluster as a VectorT.
The Size property returns the number of observations in the cluster.
The MemberIndexes
property returns a vector containing the indexes of its members in the original dataset.
The SumOfSquares returns the within-cluster
sum of squares of the distances of its members to the center. The sample code below
prints some information about each cluster. In addition, it prints out for each observation
the cluster it belongs to and the distance to its center:
foreach (var cluster in kc1.Clusters)
{
Console.WriteLine("Cluster {0} has {1} members. Sum of squares: {2:F4}",
cluster.Index, cluster.Size, cluster.SumOfSquares);
Console.WriteLine("Center: {0:F4}", cluster.Center);
}
var memberships = kc1.Predictions;
var distances = kc1.GetDistancesToCenters();
for (int i = 18; i < memberships.Length; i++)
Console.WriteLine("Observation {0} belongs to cluster {1}, distance: {2:F4}.",
i, memberships.GetLevelIndex(i), distances[i]);For Each cluster In kc1.Clusters
Console.WriteLine("Cluster {0} has {1} members. Sum of squares: {2:F4}",
cluster.Index, cluster.Size, cluster.SumOfSquares)
Console.WriteLine("Center: {0:F4}", cluster.Center)
Next
Dim memberships = kc1.Predictions
Dim distances = kc1.GetDistancesToCenters()
For i = 18 To memberships.Length - 1
Console.WriteLine("Observation {0} belongs to cluster {1}, distance: {2:F4}.",
i, memberships.GetLevelIndex(i), distances(i))
NextNo code example is currently available or this language may not be supported.
for cluster in kc1.Clusters do
printfn "Cluster %d has %d members. Sum of squares: %.4f"
cluster.Index cluster.Size cluster.SumOfSquares
printfn "Center: %A" cluster.Center
let memberships = kc1.Predictions
let distances = kc1.GetDistancesToCenters()
for i = 18 to memberships.Length-1 do
printfn "Observation %d belongs to cluster %d, distance: %.4f."
i (memberships.GetLevelIndex(i)) distances.[i]