7.00.02 - Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)
Last Update
2018-04-17

The PCA_Reduce function outputs a table in which each row represents a principal component. The rows are in descending order based on the standard deviation, which is a measure of the variation in the data set that was captured by that principal component.

PCA_Reduce Output Table Schema
Column Name Data Type Description
component_rank INTEGER Rank of the principal component. Components are ranked in descending order of standard deviation (and variance).
dimension_i DOUBLE PRECISION Values of the ith dimension of the data set. The table has one such column for each dimension.
sd DOUBLE PRECISION Standard deviation of the components in the eigenvector represented by the row.
var_proportion DOUBLE PRECISION Proportion of variance of the components in the eigenvector represented by the row.
cumulative_var DOUBLE PRECISION Cumulative variance of the components in the eigenvector represented by the row.
mean VARCHAR One row of this column contains a list of average values, one for each target_column_i in the input table. The list has this format:

[ average [, average ...] ]

The outer brackets appear in the table.

The other rows of this column contain NULL.