smile.clustering

Class CLARANS<T>

java.lang.Object

smile.clustering.PartitionClustering<T>

smile.clustering.CLARANS<T>

Type Parameters:

T - the type of input object.

All Implemented Interfaces:

java.io.Serializable, Clustering<T>

public class CLARANS<T>
extends PartitionClustering<T>
implements java.io.Serializable

Clustering Large Applications based upon RANdomized Search. CLARANS is an efficient medoid-based clustering algorithm. The k-medoids algorithm is an adaptation of the k-means algorithm. Rather than calculate the mean of the items in each cluster, a representative item, or medoid, is chosen for each cluster at each iteration. In CLARANS, the process of finding k medoids from n objects is viewed abstractly as searching through a certain graph. In the graph, a node is represented by a set of k objects as selected medoids. Two nodes are neighbors if their sets differ by only one object. In each iteration, CLARANS considers a set of randomly chosen neighbor nodes as candidate of new medoids. We will move to the neighbor node if the neighbor is a better choice for medoids. Otherwise, a local optima is discovered. The entire process is repeated multiple time to find better.

CLARANS has two parameters: the maximum number of neighbors examined (maxNeighbor) and the number of local minima obtained (numLocal). The higher the value of maxNeighbor, the closer is CLARANS to PAM, and the longer is each search of a local minima. But the quality of such a local minima is higher and fewer local minima needs to be obtained.

References

1. R. Ng and J. Han. CLARANS: A Method for Clustering Objects for Spatial Data Mining. IEEE TRANS. KNOWLEDGE AND DATA ENGINEERING, 2002.

See Also:

Serialized Form

Field Summary

Fields inherited from class smile.clustering.PartitionClustering

k, size, y

Fields inherited from interface smile.clustering.Clustering

OUTLIER

Constructor Summary

Constructors

Constructor and Description
CLARANS(T[] data, smile.math.distance.Distance<T> distance, int k) Constructor.
CLARANS(T[] data, smile.math.distance.Distance<T> distance, int k, int maxNeighbor) Constructor.
CLARANS(T[] data, smile.math.distance.Distance<T> distance, int k, int maxNeighbor, int numLocal) Constructor.

Method Summary

Modifier and Type	Method and Description
double	distortion() Returns the distortion.
int	getMaxNeighbor() Returns the maximum number of neighbors examined during a search of local minima.
int	getNumLocalMinima() Returns the number of local minima to search for.
T[]	medoids() Returns the medoids.
int	predict(T x) Cluster a new instance.
java.lang.String	toString()

Methods inherited from class smile.clustering.PartitionClustering

getClusterLabel, getClusterSize, getNumClusters, seed, seed

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

CLARANS

public CLARANS(T[] data,
               smile.math.distance.Distance<T> distance,
               int k)

Constructor. Clustering data into k clusters. The maximum number of random search is set to 0.02 * k * (n - k), where n is the number of data and k is the number clusters. The number of local searches is max(8, numProcessors).

Parameters:

data - the dataset for clustering.

distance - the distance/dissimilarity measure.

k - the number of clusters.

CLARANS

public CLARANS(T[] data,
               smile.math.distance.Distance<T> distance,
               int k,
               int maxNeighbor)

Constructor. Clustering data into k clusters.

Parameters:

data - the dataset for clustering.

distance - the distance/dissimilarity measure.

k - the number of clusters.

maxNeighbor - the maximum number of neighbors examined during a random search of local minima.

CLARANS

public CLARANS(T[] data,
               smile.math.distance.Distance<T> distance,
               int k,
               int maxNeighbor,
               int numLocal)

Constructor. Clustering data into k clusters.

Parameters:

data - the dataset for clustering.

distance - the distance/dissimilarity measure.

k - the number of clusters.

maxNeighbor - the maximum number of neighbors examined during a random search of local minima.

numLocal - the number of local minima to search for.

Method Detail

getNumLocalMinima

public int getNumLocalMinima()

Returns the number of local minima to search for.

getMaxNeighbor

public int getMaxNeighbor()

Returns the maximum number of neighbors examined during a search of local minima.

distortion

public double distortion()

Returns the distortion.

medoids

public T[] medoids()

Returns the medoids.

predict

public int predict(T x)

Cluster a new instance.

Specified by:

predict in interface Clustering<T>

Parameters:

x - a new instance.

Returns:

the cluster label, which is the index of nearest medoid.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object