Merge pull request #37 from ealcobaca/0.1.x

0.1.x

docs/source/new.rst

@@ -1,2 +1,41 @@
 What is new on pymfe package?
 #############################
+
+
+Version 0.1.0
+-------------
+- Meta-feature groups available:
+   - Relative landmarking
+   - Clustering-based
+   - Relative subsampling landmarking
+- Makefile to help developers
+
+- New Functionalities
+   - Now you can list available groups
+   - Now you can list available metafeatures
+
+- Documentation
+   - New examples
+   - New README
+
+- Bugs
+   - Problems in parse categoric metafeatures solved
+   - Categorization of attributes with constant values solved
+
+- Test
+   - Several new tests added
+
+Version 0.0.3
+-------------
+- Documentation improvement
+- Setup improvement
+
+
+Initial Release
+---------------
+- Meta-feature groups available:
+  - Simple
+  - Statistical
+  - Information-theoretic
+  - Model-based
+  - Landmarking

examples/README.txt

@@ -11,10 +11,11 @@ Measures
 
 In MtL, meta-features are designed to extract general properties able to characterize datasets. The meta-feature values should provide relevant evidences about the performance of algorithms, allowing the design of MtL-based recommendation systems. Thus, these measures must be able to predict, with a low computational cost, the performance of the algorithms under evaluation. In this package, the meta-feature measures are divided into five groups:
 
-- **General**: General information related to the dataset, also known as simple measures, such as number of instances, attributes and classes.
+- **Simple**: General information related to the dataset, also known as simple measures, such as number of instances, attributes and classes.
 - **Statistical**: Standard statistical measures to describe the numerical properties of a distribution of data.
 - **Information-theoretic**: Particularly appropriate to describe discrete (categorical) attributes and their relationship with the classes.
 - **Model-based**: Measures designed to extract characteristics like the depth, the shape and size of a Decision Tree (DT) model induced from a dataset.
-- **Landmarking**: Represents the performance of simple and efficient learning algorithms.
+- **Landmarking**: Represents the performance of simple and efficient learning algorithms. Include the subsampling and relative strategies to decrease the computation cost and enrich the relations between these meta-features (relative and subsampling landmarking are also available).
+- **Clustering**: Clustering measures extract information about dataset based on external validation indexes.
 
 Below is a gallery of examples: