Target audience: Advanced
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K-means clustering stands as a prominent unsupervised learning technique, aiming to classify unlabelled data into distinct categories. Its primary objective is to identify inherent groupings within the dataset. To achieve this, the algorithm operates in cycles, designating each data entry to a specific group based on its defining features.
This introductory piece in our series delves into the implementation of K-means' fundamental components.
Table of contents
Overview
Among the clustering methods have been developed over the years from Spectral clustering, Non-negative Matrix factorization, Canopy to Hierarchical and K-means clustering. The K-means algorithm is by far the easiest to implement. This simplicity comes with a high price in terms of scalability and even reliability. However, as an unsupervised learning technique, K-means is still valuable for reducing the number of model features or detecting anomalies.
The objective is to classify observations or data points by groups that share common attributes or features. The diagram below illustrates the clustering of observations (x,y) for a simple 2-feature model.
Each cluster has a mean or centroid, m = ( .. m..). First we need to define a distance between an observation X = (...x ..) and c. The Manhattan and Euclidean distances are respectively defined as: \[d_{M} = \sum_{i=0}^{n}\left | x_{i} - m_{i}\right |\,\,\,,\,\,d_{E}= \sum_{i=0}^{n} (x_{i} - m_{i})^{2}\] The loss function for N cluster Cj is defined by \[W(C)=\frac{1}{2}\sum_{k=0}^{N-1}\sum_{c_{i}=k}\sum_{C_{j}} d(x_{i},x_{j})\] The goal is to find the centroid m, and clusters C, that minimize the loss function as: \[C^{*}\left (i \right ) = arg\min _{k\in [0,N-1]}d (x_{i}, m_{k})\]
Note: For the sake of readability of the implementation of algorithms, all non-essential code such as error checking, comments, exception, validation of class and method arguments, scoping qualifiers or import is omitted.
Distances and observations
First we need to define the distance between each observation and the centroid of a cluster. The class hierarchy related to the distance can be implemented as nested classes as there is no reason to "expose" to client code. The interface, Distance, defines the signature of the computation method. For sake of simplicity, the sample code implements only the Manhattan and Euclidean distances. Exceptions, validation of method arguments, setter and getter methods are omitted for the sake of simplicity.
protected interface Distance {
public double compute(double[] x, Centroid centroid);
}
// Defintion of d(x,y) =|x-y|
protected class ManhattanDistance implements Distance {
public double compute(double[] x, Centroid centroid) {
double sum = 0.0, xx = 0.0;
for( int k = 0; k< x.length; k++) {
xx = x[k] - centroid.get(k);
if( xx < 0.0) {
xx = -xx;
}
sum += xx;
}
return sum;
}
}
// Definition d(x,y)= sqr(x-y)
protected class EuclideanDistance implements Distance {
public double compute(double[] x, Centroid centroid) {
double sum = 0.0, xx = 0.0;
for( int k = 0; k < x.length; k++) {
xx = x[k] - centroid.get(k);
sum += xx*xx;
}
return Math.sqrt(sum);
}
}
Next, we define an observation (or data point) as a vector or array of floating point values, in our example. An observation can support heterogeneous types (boolean, integer, float point,..) as long as they are normalized to [0,1]. In our example we simply normalized over the maximum values for all the observations.
public final class Observation {
// use Euclidean distance that is shared between all the instances
private static Distance metric = new EuclideanDistance();
public static void setMetric(final Distance metric) {
this.metric = metric;
}
private double[] _x = null;
private int _index = -1;
public Observation(double[] x, int index) {
_x = x;
_index = index;
}
// compute distance between each point and the centroid
public double computeDistance(final Centroid centroid) {
return metric.compute(_x, centroid);
}
// normalize the value of data points.
public void normalize(double[] maxValues) {
for( int k = 0; k < _x.length; k++) {
_x[k] /= maxValues[k];
}
}
}
Clustering
Centroid for each cluster are computed iteratively to reduce the loss function. The centroid values are computed using the mean of each feature across all the observations. The method Centroid.compute initialize the data points belonging to a cluster with the list of observations and compute the centroid values _x by normalizing with the number of points.
protected class Centroid {
private double[] _x = null;
protected Centroid() {}
protected Centroid(double[] x) {
Array.systemCopy(_x, x, 0, x.length, sizeOf(double));
}
// Compute the centoid values _x by normalizing with the number of points.
protected void compute(final List<Observation> observations) {
double[] x = new double[_x.length];
Arrays.fill(x, 0.0);
for( Observation point : observations ) {
for(int k =0; k < x.length; k++) {
x[k] += point.get(k);
}
}
int numPoints = observations.size();
for(int k =0; k < x.length; k++) {
_x[k] = x[k]/numPoints;
}
}
}
A cluster, KmeansCluster is defined by its label (_index in this example) a centroid, _centroid, the list of observations, _observations it contains and the current loss associated to the cluster (sum of the distance between all observations and the centroid).
The cluster behavior is defined by the following methods:
The cluster behavior is defined by the following methods:
- computeCentroid: compute the sum of the distance between all the point in this cluster and the centroid.
- attach: Attach or add a new observation to this cluster
- detach: Remove an existing observations from this cluster.
public final class KmeansCluster {
private int _index = -1;
private Centroid _centroid = null;
private double _sumDistances = 0.0;
private List<observation> _observations = new ArrayList<Observation>()
public void computeCentroid() {
_centroid.compute( _observations );
for( Observation point : _observations ) {
point.computeDistance(_centroid);
}
computeSumDistances();
}
// Attach a new observation to this cluster.
public void attach(final Observation point) {
point.computeDistance(_centroid);
_observations.add(point);
computeSumDistances();
}
public void detach(final Observation point) {
_observations.remove(point);
computeSumDistances();
}
private void computeSumDistances() {
_sumDistances = 0.0;
for( Observation point : _observations) {
_sumDistances += point.computeDistance(_centroid);
}
}
//....
}
Finally, the clustering class implements the training and run-time classification. The train method iterates across all the clusters and for all the observations to reassign the observations to each cluster. The iterative computation ends when either the loss value converges or the maximum number of iterations is reached.
If the algorithm use K clusters with M observations with N variables the execution time for creating the clusters is K*M*N. If the algorithm converges after T iterations then the overall execution is T*K*M*N. For instance, the K-means classification of 20K observations and data with 25 dimension, using 10 clusters, converging after 50 iterations requires 250,000,000 evaluations! The constructor create the clustering algorithm with a predefined number of cluster, K, and a set of observations.
The method getCentroids retrieves the current list of centroids (value of centroid vectors)
public final class KmeansClustering {
private KmeansCluster[] _clusters = null;
private Observation[] _obsList = null;
private double _totalDistance = 0.0;
private Centroid[] _centroids = null;
public KmeansClustering(int numClusters, final Observation[] obsList) {
_clusters = new KmeansCluster[numClusters];
for (int i = 0; i < numClusters; i++) {
_clusters[i] = new KmeansCluster(i);
}
_obsList = obsList;
}
public final List<double[]> getCentroids() {
List<double[]> centroidDataList = null;
if(_clusters != null &&; _clusters.length < 0) {
centroidDataList = new LinkedList<double[]>();
for( KmeansCluster cluster : _clusters) {
centroidDataList.add(cluster.getCentroid().getX());
}
}
return centroidDataList;
}
}
The next article, K-means clustering in Java: Classification describes the implementation of the training and classification tasks.
References
- The Elements of Statistical Learning - T. Hastie, R.Tibshirani, J. Friedman - Springer 2001
- Machine Learning: A Probabilisitc Perspective 11.4.2.5 K-means algorithm - K. Murphy - MIT Press 2012
- Pattern Recognition and Machine Learning: Chap 9 "Mixture Models and EM: K-means Clustering" C.Bishop - Springer Science 2006
- github.com/patnicolas
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Patrick Nicolas has over 25 years of experience in software and data engineering, architecture design and end-to-end deployment and support with extensive knowledge in machine learning.
He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning" Packt Publishing ISBN 978-1-78712-238-3
He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning" Packt Publishing ISBN 978-1-78712-238-3
