de.aitools.dm.clustering.algorithms
Class TestKNNHAC
java.lang.Object
de.aitools.dm.clustering.algorithms.TestKNNHAC
public final class TestKNNHAC
- extends java.lang.Object
EXAMPLE_DATA is taken from:
Tan Pang-Ning, Steinbach Michael, Kumar Vipin (2006).
Introduction to Data Mining.
Pearson Education, Boston, MA.
BibTeX:
@book{TanSteinbachKumar2006,
address = {Boston, MA},
author = {Tan Pang-Ning and Steinbach Michael and Kumar Vipin},
publisher = {Pearson Education},
title = {Introduction to Data Mining},
year = {2006}
}
Note that in the book the data indices starts with 1, while in this array
implementation they of course start with 0. Also note that in the book
distances were used instead of similarities. However, in the implementation
of EuclideanDistance
this is just the negative value.
Finally sim(p2,p3) was sim(p3,p4) (book indices) such that a other
valid
clustering may be build. Thus p3 was changed.
TODO: Some methods are missing. However, they depend on the coefficients.
- Version:
- $Id: TestKNNHAC.java,v 1.1 2011/06/22 14:22:51 dogu3912 Exp $
- Author:
- johannes.kiesel(/\t)uni-weimar.de
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
TestKNNHAC
public TestKNNHAC()
testKNN
public void testKNN()
testHACTestsSingleLinkEuclideanDistance
public void testHACTestsSingleLinkEuclideanDistance()
testDendrogramSingleLink
public void testDendrogramSingleLink()
testCluster
public void testCluster()
testClusterDendrogramWeightedAverage
public void testClusterDendrogramWeightedAverage()
testClusterDendrogramCompleteLink
public void testClusterDendrogramCompleteLink()
testClusterDendrogramSingleLink
public void testClusterDendrogramSingleLink()