7.00.02 - Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

The FPGrowth function outputs either a pattern table, a rule table, or both (depending on the value of the PatternsOrRules argument).

The following table describes its columns of the pattern table.

FPGrowth Pattern Table Schema
Column Name Data Type Description
group_by_column Same as input Column copied from input table
pattern_item_column VARCHAR Pattern composed of transaction items
length_of_pattern INTEGER Number of items in pattern
count BIGINT Count of occurrence of pattern
support DOUBLE PRECISION Percentage of transactions that contain the pattern: count/t, where t is the number of transactions.

For example, if eggs and milk were purchased together 3 times in 5 transactions, then the support value is 3/5, 60%.

The output has one row for each rule. The following table describes its columns.

FPGrowth Rule Table Schema
Column Name Data Type Contents
antecedent_item_column VARCHAR Items in the antecedent of the rule.
consequence_item_column VARCHAR Items in the consequence of the rule.
count_of_antecedent INTEGER Count of items in the antecedent of the rule.
count_of_consequence INTEGER Count of items in the consequence of the rule.
cntb BIGINT Count of transactions that contain both the antecedent and consequence.
cnt_antecedent BIGINT Count of transactions that contain the antecedent.
cnt_consequence BIGINT Count of transactions that contain the consequence.
score DOUBLE PRECISION Product of two conditional probabilities:

(cntb / cnt_antecedent) * (cntb / cnt_consequence)

support DOUBLE PRECISION Percentage of transactions that contain both the antecedent and consequence: cntb/t, where t is the number of transactions.

For example, if eggs and milk were purchased together 3 times in 5 transactions, then the support value is 3/5, 60%.

confidence DOUBLE PRECISION Percentage of transactions that contain the antecedent that also contain the consequence:

cntb / cnt_antecedent

For example, for the antecedent milk and consequence butter, if cntb=3 and cnt_antecedent=4, then the confidence value is 3/4, 75%. In other words, 75% of the time, when a person buys milk, the person also buys butter.

lift DOUBLE PRECISION Ratio of the observed support value to the expected support value if the antecedent and consequence are independent:

(cntb/t) / ((cnt_antecedent/t) * (cnt_consequence/t))

conviction DOUBLE PRECISION More reliable alternative to confidence:

(1-cnt_consequence/t) / (1-cntb/cnt_antecedent)

leverage DOUBLE PRECISION Difference between the percentage of transactions that contain both the antecedent and consequence (cntb/t) and the expectation for cntb/t if the antecedent and consequence were statistically independent:

(cntb/t) - ((cnt_antecedent/t) * (cnt_consequence/t))

coverage DOUBLE PRECISION Percentage of transactions in which the rule applies:

cnt_antecedent/t

Another name for coverage is antecedent support.

chi_square DOUBLE PRECISION Chi-squared test result, used to test the hypothesis that the antecedent and consequence are not associated. The formula follows this table.
z_score DOUBLE PRECISION Significance of cntb, assuming that it follows a normal distribution:

(cntb - mean(cntb)) / standard_deviation(cntb)

If every cntb is the same, then standard_deviation(cntb) is 0, and the function does not compute z_score.

The formula for the value of chi_square is:

(t * (cntb * (t + cntb - cnt_antecedent - cnt_consequence) - (cnt_antecedent - cntb) *

  (cnt_consequence - cntb))**2) /

  (cnt_antecedent * (t - cnt_antecedent) * cnt_consequence * (t - cnt_consequence))