switched to new ml.cms.waikato.ac.nz links

docs/academic/related_projects.md

@@ -48,10 +48,10 @@ You may also want to take a look at the (incomplete) list of ["unofficial" packa
 * [StarSystem](https://github.com/elettrotecnica/starsystem) command-line tool implementing best practices in supervised classification, including "agnostic" feature selection.
 * [TClass](https://github.com/fracpete/tclass-weka-package) - classifying multivariate time series.
 * [Tertius](http://www.cs.bris.ac.uk/research/machinelearning/tertius/) - a system for rule discovery.
-* [TUBE](https://www.cs.waikato.ac.nz/ml/weka/TUBE/) - Tree-based Density Estimation Algorithms.
+* [TUBE](https://ml.cms.waikato.ac.nz/weka/TUBE/) - Tree-based Density Estimation Algorithms.
 * [TunedIT](http://tunedit.org/) - Automated tests of machine-learning algorithms. Repository of datasets, algorithms and benchmarks.
 * [Weka for Computational Genetics](http://sourceforge.net/projects/wekacg) - Multifactor Dimensionality Reduction (MDR) added to the Weka package.
-* [Weka Proper](https://www.cs.waikato.ac.nz/ml/proper/)- Database propositionalization for Weka.
+* [Weka Proper](https://ml.cms.waikato.ac.nz/proper/)- Database propositionalization for Weka.
 * [weka4WS](http://gridlab.dimes.unical.it/weka4ws/) - distributed data mining.
 * [Weka-GDPM and Weka-STPM](http://www.inf.ufsc.br/~vania/software.html) - Weka for geographic data processing and Moving Object Data Analysis and Mining.
 * [WekaMetal](http://www.cs.bris.ac.uk/research/machinelearning/wekametal/) - a meta-learning extension to Weka.

docs/citing_weka.md

@@ -1,6 +1,6 @@
 The best reference for WEKA 3.8 and 3.9 is the [online
 appendix on the WEKA
-workbench](https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf)
+workbench](https://ml.cms.waikato.ac.nz/weka/Witten_et_al_2016_appendix.pdf)
 for the fourth edition of "Data Mining: Practical Machine Learning
 Tools and Techniques" by I.H. Witten, Eibe Frank, Mark A. Hall, and
 Chris J. Pal.  The citation is

docs/datasets.md

@@ -4,7 +4,7 @@ Some example datasets for analysis with Weka are included in the Weka distributi
 
 * A jarfile containing 37 classification problems originally obtained from the [UCI repository of machine learning datasets](https://www.ics.uci.edu/~mlearn/mlrepository.html) ([datasets-UCI.jar](https://prdownloads.sourceforge.net/weka/datasets-UCI.jar), 1,190,961 Bytes).
 * A jarfile containing 37 regression problems obtained from various sources ([datasets-numeric.jar](https://prdownloads.sourceforge.net/weka/datasets-numeric.jar), 169,344 Bytes).
-* A jarfile containing 6 agricultural datasets obtained from agricultural researchers in New Zealand ([agridatasets.jar](https://www.cs.waikato.ac.nz/~ml/weka/agridatasets.jar), 31,200 Bytes).
+* A jarfile containing 6 agricultural datasets obtained from agricultural researchers in New Zealand ([agridatasets.jar](https://ml.cms.waikato.ac.nz/weka/agridatasets.jar), 31,200 Bytes).
 * A jarfile containing 30 regression datasets collected by [Professor Luis Torgo](https://web.cs.dal.ca/~ltorgo/) ([regression-datasets.jar](https://prdownloads.sourceforge.net/weka/regression-datasets.jar), 10,090,266 Bytes).
 * A gzip'ed tar containing [UCI ML](https://www.ics.uci.edu/~mlearn/MLRepository.html) and [UCI KDD](https://kdd.ics.uci.edu/) datasets ([uci-20070111.tar.gz](https://prdownloads.sourceforge.net/weka/uci-20070111.tar.gz), 17,952,832  Bytes)
 * A gzip'ed tar containing [StatLib](http://lib.stat.cmu.edu/datasets/) datasets ([statlib-20050214.tar.gz](https://prdownloads.sourceforge.net/weka/statlib-20050214.tar.gz), 12,785,582 Bytes)

docs/documentation.md

@@ -3,25 +3,25 @@ software. Weka comes with built-in help and includes a comprehensive
 manual. For an introduction to the machine learning techniques
 implemented in Weka, and the software itself, consider taking a look
 at the book [Data Mining: Practical Machine Learning Tools and
-Techniques](https://www.cs.waikato.ac.nz/~ml/weka/book.html) and its
+Techniques](https://ml.cms.waikato.ac.nz/weka/book.html) and its
 freely available [online appendix on the Weka
-workbench](https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf),
+workbench](https://ml.cms.waikato.ac.nz/weka/Witten_et_al_2016_appendix.pdf),
 providing an overview of the software. Closely linked to the book,
 there are also free [online
-courses](https://www.cs.waikato.ac.nz/~ml/weka/courses.html) on data
+courses](https://ml.cms.waikato.ac.nz/weka/courses.html) on data
 mining with the machine learning techniques in Weka. A list of sources
 with information on Weka is provided below.
 
 	    
 # General documentation
 
-* The online appendix [The Weka Workbench](https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf), distributed as a free PDF, for the fourth edition of the book [Data Mining: Practical Machine Learning Tools and Techniques](https://www.cs.waikato.ac.nz/~ml/weka/book.html).
+* The online appendix [The Weka Workbench](https://ml.cms.waikato.ac.nz/weka/Witten_et_al_2016_appendix.pdf), distributed as a free PDF, for the fourth edition of the book [Data Mining: Practical Machine Learning Tools and Techniques](https://ml.cms.waikato.ac.nz/weka/book.html).
 
 * The [manual for Weka 3.8](https://prdownloads.sourceforge.net/weka/WekaManual-3-8-3.pdf?download) and the [manual for Weka 3.9](https://prdownloads.sourceforge.net/weka/WekaManual-3-9-3.pdf?download), as included in the distribution of the software when you download it.
 
 * The [Javadoc for Weka 3.8](https://weka.sourceforge.io/doc.stable-3-8/) and the [Javadoc for Weka 3.9](https://weka.sourceforge.io/doc.dev/), extracted directly from the source code, providing information on the API and parameters for command-line usage of Weka.
 
-* The videos and slides for the online courses on [Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/), [More Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/moredataminingwithweka/), and [Advanced Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/advanceddataminingwithweka/).
+* The videos and slides for the online courses on [Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/dataminingwithweka/), [More Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/moredataminingwithweka/), and [Advanced Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/advanceddataminingwithweka/).
 
 # Weka packages
 

docs/downloading_weka.md

@@ -21,7 +21,7 @@ source code is taken, compiled, and put together in ZIP files. This
 happens for both the development branch of the software and the stable
 branch. Those who want the latest bug fixes before the next official
 release is made can download these
-[snapshots](https://www.cs.waikato.ac.nz/~ml/weka/snapshots/weka_snapshots.html).
+[snapshots](https://ml.cms.waikato.ac.nz/weka/snapshots/weka_snapshots.html).
 
 # Stable version
 
@@ -162,7 +162,7 @@ Weka 3.8 package manager does not start up, please delete the file
 `installedPackageCache.ser` in the `packages` folder that resides in
 the `wekafiles` folder in your user home. Also, serialized Weka models
 created in 3.7 are incompatible with 3.8. The [model
-migrator](https://www.cs.waikato.ac.nz/~ml/weka/modelMigrator.jar)
+migrator](https://ml.cms.waikato.ac.nz/weka/modelMigrator.jar)
 tool can migrate some models to 3.8 (a known
 exception is RandomForest). Usage is as follows:
 

docs/getting_help.md

@@ -12,7 +12,7 @@ you can see the entire error output from Java (including the Java
 stack trace). This makes it much more likely that you will get useful
 help.  When posting questions, comments, or bug reports to the Weka
 mailing list, consider the [mailing list
-etiquette](https://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html).
+etiquette](https://ml.cms.waikato.ac.nz/weka/mailinglist_etiquette.html).
 
 # Mailing list archive and mirrors
 

docs/learning_resources.md

@@ -10,6 +10,6 @@
 
 # MOOCs
 
-* [Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/)
-* [More Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/moredataminingwithweka/)
-* [Advanced Data Mining with Weka](https://www.cs.waikato.ac.nz/ml/weka/mooc/advanceddataminingwithweka/)
+* [Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/dataminingwithweka/)
+* [More Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/moredataminingwithweka/)
+* [Advanced Data Mining with Weka](https://ml.cms.waikato.ac.nz/weka/mooc/advanceddataminingwithweka/)

docs/literature.md

@@ -22,4 +22,4 @@ Apart from [Data Mining: Practical Machine Learning Tools and Techniques](https:
 
 * Hongbo Du (2010) [Data Mining Techniques and Applications](https://cengage.co.nz/product/title/data-mining-techniques-and-applications/isbn/9781844808915), Cengage Learning.
 
-* A book explaining why [Weka won't learn](https://www.cs.waikato.ac.nz/~ml/images/weka_wont_learn.gif) (discovered by Stuart Inglis).
+* A book explaining why [Weka won't learn](https://ml.cms.waikato.ac.nz/images/weka_wont_learn.gif) (discovered by Stuart Inglis).

docs/mailing_list.md

@@ -4,4 +4,4 @@ The WEKA Mailing list can be found here:
 * [Archives](https://list.waikato.ac.nz/hyperkitty/list/wekalist@list.waikato.ac.nz/) ([Mirror 1](https://weka.8497.n7.nabble.com/), [Mirror 2](https://marc.info/?l=wekalist)) for searching previous posted messages.
 
 
-Before posting, please read the [mailing list etiquette](https://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html).
+Before posting, please read the [mailing list etiquette](https://ml.cms.waikato.ac.nz/weka/mailinglist_etiquette.html).

docs/message_classifier.md

@@ -6,9 +6,9 @@ In the following you'll find some information about the MessageClassifier from t
 Depending on the version of the book, download the corresponding version (this article is based on the **2nd** edition):
 
 * 1st Edition: 
-[MessageClassifier](https://www.cs.waikato.ac.nz/~ml/weka/example_code/MessageClassifier.java)
+[MessageClassifier](https://ml.cms.waikato.ac.nz/weka/example_code/MessageClassifier.java)
 * 2nd Edition: 
-[MessageClassifier](https://www.cs.waikato.ac.nz/~ml/weka/example_code/2ed/MessageClassifier.java) ([book](https://git.cms.waikato.ac.nz/weka/weka/-/tree/book2ndEd-branch/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java), [stable-3.8](https://git.cms.waikato.ac.nz/weka/weka/-/tree/stable-3-8/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java), [developer](https://git.cms.waikato.ac.nz/weka/weka/-/tree/main/trunk/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java))
+[MessageClassifier](https://ml.cms.waikato.ac.nz/weka/example_code/2ed/MessageClassifier.java) ([book](https://git.cms.waikato.ac.nz/weka/weka/-/tree/book2ndEd-branch/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java), [stable-3.8](https://git.cms.waikato.ac.nz/weka/weka/-/tree/stable-3-8/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java), [developer](https://git.cms.waikato.ac.nz/weka/weka/-/tree/main/trunk/wekaexamples/src/main/java/wekaexamples/book/MessageClassifier.java))
 
 # Compiling
 * compile the source code like this, if the `weka.jar` is already in your [CLASSPATH](classpath.md) environment variable: