This is the cl-association-rules Reference Manual, version 0.0.1, generated automatically by Declt version 4.0 beta 2 "William Riker" on Sat Dec 03 19:51:41 2022 GMT+0.
The main system appears first, followed by any subsystem dependency.
cl-association-rulesAn implementation of the apriori algorithm to mine association rules in Common Lisp.
Diogo Franco
MIT
0.0.1
package.lisp (file).
rule.lisp (file).
apriori.lisp (file).
Files are sorted by type and then listed depth-first from the systems components trees.
cl-association-rules/cl-association-rules.asdcl-association-rules/package.lispcl-association-rules/rule.lispcl-association-rules/apriori.lispcl-association-rules/cl-association-rules.asdcl-association-rules (system).
cl-association-rules/package.lispcl-association-rules (system).
cl-association-rules/rule.lisppackage.lisp (file).
cl-association-rules (system).
make-rule (function).
print-object (method).
rule-confidence (reader).
(setf rule-confidence) (writer).
rule-posttuple (reader).
(setf rule-posttuple) (writer).
rule-pretuple (reader).
(setf rule-pretuple) (writer).
rule-support (reader).
(setf rule-support) (writer).
cl-association-rules/apriori.lisprule.lisp (file).
cl-association-rules (system).
apriori (function).
generate-candidates (function).
generate-rules (function).
get-all-items (function).
get-frequent-items (function).
set-equal-p (function).
Packages are listed by definition order.
cl-association-rulesrules
common-lisp.
apriori (function).
make-rule (function).
rule-confidence (reader).
(setf rule-confidence) (writer).
rule-posttuple (reader).
(setf rule-posttuple) (writer).
rule-pretuple (reader).
(setf rule-pretuple) (writer).
rule-support (reader).
(setf rule-support) (writer).
copy-rule (function).
generate-candidates (function).
generate-rules (function).
get-all-items (function).
get-frequent-items (function).
rule (structure).
rule-p (function).
set-equal-p (function).
Definitions are sorted by export status, category, package, and then by lexicographic order.
Calculates the association rules in the dataset using the apriori
algorithm. Expects a dataset of the form
((1 2 3 4)
(3 2 7 9)
(9)
(2 3 8)
(2 0)),
where each line is a transaction.
Returns a list of the mined rules.
Generates the next candidate set, from the previous frequent and non-frequent itemsets.
From the frequent itemsets found by apriori, generate association rules for a given min-confidence.
From a dataset, return the unique items as a list.
From a candidate set and support threshold, return the items that are frequent and non-frequent in the dataset.
Returns t if two sets of elements are equal
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