Principal Component Analysis
Runs Principal Component Analysis, which converts data so that observations (rows) are expressed in a set of variables called principal components. Principal components tries to keep as much information of the original data in as few variables as possible.
Input data should contain following columns.
- Variable Columns - Set of numeric columns to convert into principal components.
- Color By - Value of the column is used for color of each dot on the Scatter View.
- Label Column - Value of the column is shown on the Scatter View as label of each dot, or as an item on mouse-over balloon.
- Data Preprocessing
- Sample Data Size - Number of rows to sample before clustering them.
- Random Seed - Seed used to generate random numbers. Specify this value to always reproduce the same result.
- Normalize Variable Columns - Whether to normalize Variable Columns before running the analysis.
- 1.Click Analytics View tab.
- 2.If necessary, click "+" button on the left of existing Analytics tabs, to create a new Analytics.
- 3.Select "Principal Component Analysis" for Type.
- 4.Select Variable Columns.
- 5.Click Run button to run the analytics.
- 6.Select view type (explained below) by clicking view type link to see each type of generated visualization.
Last modified 3yr ago