XMeans (3.6) (legacy)

Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, 2000.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify clusterer-specific parameters.

Options

Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Numeric attributes, Date attributes, Missing values, No class] Dependencies: [] min # Instance: 1

Clusterer Options

I: maximum number of overall iterations (default 1).

M: maximum number of iterations in the kMeans loop in the Improve-Parameter part (default 1000).

J: maximum number of iterations in the kMeans loop for the splitted centroids in the Improve-Structure part (default 1000).

L: minimum number of clusters (default 2).

H: maximum number of clusters (default 4).

B: distance value for binary attributes (default 1.0).

use-kdtree: Uses the KDTree internally (default no).

K: Full class name of KDTree class to use, followed by scheme options. eg: "weka.core.neighboursearch.kdtrees.KDTree -P" (default no KDTree class used).

C: cutoff factor, takes the given percentage of the splitted centroids if none of the children win (default 0.0).

D: Full class name of Distance function class to use, followed by scheme options. (default weka.core.EuclideanDistance).

N: file to read starting centers from (ARFF format).

O: file to write centers to (ARFF format).

U: The debug level. (default 0)

Y: The debug vectors file.

S: Random number seed. (default 10)

Input Ports

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Training data

Output Ports

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Trained clusterer

Popular Predecessors

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Popular Successors

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Views

Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Workflows

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Links

Developers

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Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 2.10.2.v202312081535
On NodePit since: 2024-07-09
Last update: 2024-11-21
KNIME versions: Since v5.2

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