Merge pull request #8 from ajdapretnar/docs-unsup

Unsupervised: Updated documentation

Author: janezd

source/index.rst

@@ -57,11 +57,9 @@ Data
    widgets/data/applydomain
    widgets/data/purgedomain
    widgets/data/rank
-   widgets/data/correlations
    widgets/data/color
    widgets/data/featurestatistics
    widgets/data/melt
-   widgets/data/neighbors
    widgets/data/unique
    widgets/data/groupby
 
@@ -150,6 +148,8 @@ Unsupervised
    :maxdepth: 1
 
    widgets/unsupervised/PCA
+   widgets/unsupervised/neighbors
+   widgets/unsupervised/correlations
    widgets/unsupervised/correspondenceanalysis
    widgets/unsupervised/distancemap
    widgets/unsupervised/distances

source/widgets/data/images/Correlations-Example.png

@@ -10,20 +10,19 @@ PCA linear transformation of input data.
 **Outputs**
 
 - Transformed Data: PCA transformed data
-- Components: [Eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors).
+- Data: original data with PCA components as meta variables
+- Components: [Eigenvectors](https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors)
+- PCA: PCA to use as Scorer in [Rank](../data/rank.md)
 
 [Principal Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA) computes the PCA linear transformation of the input data. It outputs either a transformed dataset with weights of individual instances or weights of principal components.
 
-![](images/PCA-stamped.png)
+![](images/PCA-stamped.png){width=500px}
 
-1. Select how many principal components you wish in your output. It is best to choose as few as possible with variance covered as high as possible. You can also set how much variance you wish to cover with your principal components.
-2. You can normalize data to adjust the values to common scale. If checked, columns are divided by their standard deviations.
+1. Select how many principal components you wish in your output. It is best to choose as few as possible with variance (parameter *Explained variance*) covered as high as possible. You can also set how much variance you wish to cover with your principal components.
+2. You can normalize data to adjust the values to common scale. If checked, columns are divided by their standard deviations. One can also set how many components to display in the graph.
 3. When *Apply Automatically* is ticked, the widget will automatically communicate all changes. Alternatively, click *Apply*.
-4. Press *Save Image* if you want to save the created image to your computer.
-5. Produce a report.
-6. Principal components graph, where the red (lower) line is the variance covered per component and the green (upper) line is cumulative variance covered by components.
 
-The number of components of the transformation can be selected either in the *Components Selection* input box or by dragging the vertical cutoff line in the graph.
+The principal components graph, called a scree plot, show the red (lower) line, representing the variance covered per component, and the green (upper) line, representing the cumulative variance covered by components. The number of components of the transformation can be selected either in the *Components* input box or by dragging the vertical cutoff line in the graph.
 
 Preprocessing
 -------------
@@ -39,8 +38,8 @@ Examples
 
 **PCA** can be used to simplify visualizations of large datasets. Below, we used the *Iris* dataset to show how we can improve the visualization of the dataset with PCA. The transformed data in the [Scatter Plot](../visualize/scatterplot.md) show a much clearer distinction between classes than the default settings.
 
-![](images/PCAExample.png)
+![](images/PCA-Example1.png)
 
-The widget provides two outputs: transformed data and principal components. Transformed data are weights for individual instances in the new coordinate system, while components are the system descriptors (weights for principal components). When fed into the [Data Table](../data/datatable.md), we can see both outputs in numerical form. We used two data tables in order to provide a more clean visualization of the workflow, but you can also choose to edit the links in such a way that you display the data in just one data table. You only need to create two links and connect the *Transformed data* and *Components* inputs to the *Data* output.
+PCA can also be used as a scorer for the [Rank](../data/rank.md) widget. We used the *iris* data for this example. The data is passed both to Rank and to PCA. PCA passes the Scorer output to the Rank widget. Rank now shows feature scores for the first two principal components.
 
-![](images/PCAExample2.png)
+![](images/PCA-Example2.png)