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Miran Levar committed 5009dfc

Documentation update

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 extends Orange by providing methods that allow for classification of datasets
 with multiple classes.
 
-Currently supported techniques: clustering trees, PLS, binary relevance, chain classification and neural networks (for multi-label data only).
-
+Currently supported techniques: 
+	
+	* Binary Relevance
+	* Classigier Chains
+	* Clustering Trees	
+	* Neural Networks
+	* Partial Least Squares
 
 
 .. _Orange: http://orange.biolab.si/
 
-Documentation will be found at:
+Documentation can be viewed at:
 
 http://orange-multitarget.readthedocs.org/
 

_multitarget/tree.py

 
 :obj:`ClusteringTreeLearner` is an implementation of classification and regression
 trees, based on the :obj:`SimpleTreeLearner`. It is implemented in C++ for speed and low memory usage.
-Features are selected by finding the furthest apart clusters measured with the euclidean distance between prototypes, 
-which are the means of clusters.
+Clustering trees work by splitting the data into clusters based on attributes. The attribute provides the optimal split based on a measure, 
+the default used in this implementation is the Euclidean distance between the centroids of clusters, which we try to maximize.
 
 :obj:`ClusteringTreeLearner` was developed for speeding up the construction
 of random forests, but can also be used as a standalone tree learner.
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