Last updated: Jan 18, 2024
The Apriori node extracts a set of rules from the data, pulling out the rules
with the highest information content. Apriori offers five different methods of selecting rules and
uses a sophisticated indexing scheme to process large data sets efficiently. For large problems,
Apriori is generally faster to train; it has no arbitrary limit on the number of rules that can be
retained, and it can handle rules with up to 32 preconditions. Apriori requires that input and
output fields all be categorical but delivers better performance because it'ss optimized for this
type of data.
Example
node = stream.create("apriori", "My node")
# "Fields" tab
node.setPropertyValue("custom_fields", True)
node.setPropertyValue("partition", "Test")
# For non-transactional
node.setPropertyValue("use_transactional_data", False)
node.setPropertyValue("consequents", ["Age"])
node.setPropertyValue("antecedents", ["BP", "Cholesterol", "Drug"])
# For transactional
node.setPropertyValue("use_transactional_data", True)
node.setPropertyValue("id_field", "Age")
node.setPropertyValue("contiguous", True)
node.setPropertyValue("content_field", "Drug")
# "Model" tab
node.setPropertyValue("use_model_name", False)
node.setPropertyValue("model_name", "Apriori_bp_choles_drug")
node.setPropertyValue("min_supp", 7.0)
node.setPropertyValue("min_conf", 30.0)
node.setPropertyValue("max_antecedents", 7)
node.setPropertyValue("true_flags", False)
node.setPropertyValue("optimize", "Memory")
# "Expert" tab
node.setPropertyValue("mode", "Expert")
node.setPropertyValue("evaluation", "ConfidenceRatio")
node.setPropertyValue("lower_bound", 7)
apriorinode Properties |
Values | Property description |
|---|---|---|
consequents
|
field | Apriori models use Consequents and Antecedents in place of the standard target and input fields. Weight and frequency fields are not used. See Common modeling node properties for more information. |
antecedents
|
[field1 ... fieldN] | |
min_supp
|
number | |
min_conf
|
number | |
max_antecedents
|
number | |
true_flags
|
flag | |
optimize
|
Speed
Memory
|
|
use_transactional_data
|
flag | |
contiguous
|
flag | |
id_field
|
string | |
content_field
|
string | |
mode
|
Simple
Expert
|
|
evaluation
|
RuleConfidence
DifferenceToPrior
ConfidenceRatio
InformationDifference
NormalizedChiSquare
|
|
lower_bound
|
number | |
optimize
|
Speed
Memory
|
Use to specify whether model building should be optimized for speed or for memory. |
rules_without_antececents |
boolean | Select to allow rules that include only the consequent (item or item set). This is useful
when you are interested in determining common items or item sets. For example,
cannedveg is a single-item rule without an antecedent that indicates purchasing
cannedveg is a common occurrence in the data. |