Arguments - Aster Analytics

Teradata Aster Analytics Foundation User Guide

Product
Aster Analytics
Release Number
6.21
Published
November 2016
Language
English (United States)
Last Update
2018-04-14
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kiu1466024880662.ditamap
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dita:id
B700-1021
lifecycle
previous
Product Category
Software
Argument Category Description
InputTable Required Specifies the name of the table that contains the object pairs and their field-pair similarity values.
ComparisonFields Required Specifies the columns of input_table to use in the field-pair similarity in the training process. If the value in the column is less than threshold_value, then the field pair does not agree; otherwise, the field pair agrees. The default value of threshold_value is 1.
TagColumn Optional If you specify this argument, then the function uses supervised learning; if you omit it, then the function uses unsupervised learning.

This argument specifies the name of the column that indicates whether two objects match. The column must contain only the values 'M' (matched) and 'U' (unmatched).

InitialM Optional For unsupervised learning, this argument specifies the initial value of m, which is the probability that a field agrees, given that the object-pair belongs to the same object. The default value is 0.9.

For supervised learning, the function ignores this argument.

InitialU Optional For unsupervised learning, this argument specifies the initial value of u, which is the probability that a field agrees, given that the object-pair belongs to a different object. The default value is 0.1.

For supervised learning, the function ignores this argument.

InitialP Optional For unsupervised learning, this argument specifies the initial value of p, which is the percentage of all possible object-pairs that contain the same object. The default value is 0.1.

For supervised learning, the function ignores this argument.

MaxIteration Optional For unsupervised learning, this argument specifies the maximum number of iterations. The default value is 100.

For supervised learning, the function ignores this argument.

Eta Optional For unsupervised learning, this argument specifies the tolerance of the termination criterion. At the end of each iteration, the function computes the difference between the current value of p and the value of p at the end of the previous iteration. If the difference is less than eta_value, then the function terminates.The default value is 1*10-5.
Lambda Optional Specifies the Type I (false negative) error, which occurs if an unmatched comparison is erroneously linked. The default value is 0.9.
Mu Optional Specifies the Type II (false positive) error, which occurs if a matched comparison is erroneously not linked. The default value is 0.9.
Lambda and Mu determine the values of the model properties lower_bound and upper_bound. For details, see: Fellegi, Ivan; Sunter, Alan (December 1969). "A Theory for Record Linkage" Journal of the American Statistical Association 64