Frequent pattern growth

Supported in: Batch

Frequent pattern (fp) growth finds frequent patterns in your dataset.

Transform categories: Aggregate, Other

Declared arguments

Input dataset - Source dataset containing items columns and transaction column.
Table
Items column - Array column containing the items for the patterns.
Column<Array<String>>
Minimum support - Minimum fraction of how often a pattern needs to be present.
Literal<Double>

Argument values:

Input:

customer_attributes
[ age_group: 20-30, country: Germany, gender: Female ]
[ age_group: 20-30, country: Germany, gender: Male ]

Output:

Argument values:

Input:

customer_attributes
null

Output:

pattern	pattern_occurrence	total_count

Argument values:

Input:

customer_attributes
[ age_group: 20-30, country: Germany, gender: Female ]
[ null ]

Output:

pattern	pattern_occurrence	total_count
[ country: Germany ]	1	2
[ country: Germany, age_group: 20-30 ]	1	2
[ null ]	1	2
[ age_group: 20-30 ]	1	2
[ gender: Female ]	1	2
[ gender: Female, country: Germany ]	1	2
[ gender: Female, country: Germany, age_group: 20-30 ]	1	2
[ gender: Female, age_group: 20-30 ]	1	2

Argument values:

Input:

customer_attributes
[ age_group: 20-30, country: Germany, gender: Female ]
[ age_group: 20-30, country: Germany, gender: Male ]

Output:

pattern	pattern_occurrence	total_count
[ gender: Male ]	1	2
[ gender: Male, country: Germany ]	1	2
[ gender: Male, country: Germany, age_group: 20-30 ]	1	2
[ gender: Male, age_group: 20-30 ]	1	2
[ age_group: 20-30 ]	2	2
[ country: Germany ]	2	2
[ country: Germany, age_group: 20-30 ]	2	2
[ gender: Female ]	1	2
[ gender: Female, country: Germany ]	1	2
[ gender: Female, country: Germany, age_group: 20-30 ]	1	2
[ gender: Female, age_group: 20-30 ]	1	2