Download all versions of classificationViaClustering JAR files with all dependencies
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.7)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.7
Last update 26. November 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.7
Last update 26. November 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.6)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 29. May 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.6
Last update 29. May 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.5)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 27. April 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.5
Last update 27. April 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.4)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 04. April 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.4
Last update 04. April 2017
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.3)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 28. April 2014
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 28. April 2014
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.2)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
classificationViaClustering from group nz.ac.waikato.cms.weka (version 1.0.1)
A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
2 downloads
Artifact classificationViaClustering
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 23. April 2012
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.1
Last update 23. April 2012
Tags: model hall generate simple simplekmeans same fixed meta missing clusterers time order value useful algorithms instance note sure classes functionality weka labels prediction class number mark code like that obtain clusters dataset classification classifier cluster uses clusterevaluation make user returned clusterer based found
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classificationViaClustering
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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