Open main menu
Home
Random
Recent changes
Special pages
Community portal
Preferences
About Wikipedia
Disclaimers
Incubator escapee wiki
Search
User menu
Talk
Dark mode
Contributions
Create account
Log in
Editing
Association rule learning
(section)
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
{{Short description|Method for discovering interesting relations between variables in databases}} {{Machine learning|Problems}} '''Association rule learning''' is a [[rule-based machine learning]] method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.<ref name="piatetsky">Piatetsky-Shapiro, Gregory (1991), ''Discovery, analysis, and presentation of strong rules'', in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., ''Knowledge Discovery in Databases'', AAAI/MIT Press, Cambridge, MA.</ref> In any given transaction with a variety of items, association rules are meant to discover the rules that determine how or why certain items are connected. Based on the concept of strong rules, [[Rakesh Agrawal (computer scientist)|Rakesh Agrawal]], [[Tomasz Imieliński]] and Arun Swami<ref name="mining">{{Cite book | last1 = Agrawal | first1 = R. | last2 = Imieliński | first2 = T. | last3 = Swami | first3 = A. | doi = 10.1145/170035.170072 | chapter = Mining association rules between sets of items in large databases | title = Proceedings of the 1993 ACM SIGMOD international conference on Management of data - SIGMOD '93 | pages = 207 | year = 1993 | isbn = 978-0897915922 | citeseerx = 10.1.1.40.6984 | s2cid = 490415 }}</ref> introduced association rules for discovering regularities between products in large-scale transaction data recorded by [[point-of-sale]] (POS) systems in supermarkets. For example, the rule <math>\{\mathrm{onions, potatoes}\} \Rightarrow \{\mathrm{burger}\}</math> found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional [[pricing]] or [[product placement]]s. In addition to the above example from [[market basket analysis]], association rules are employed today in many application areas including [[Web usage mining]], [[intrusion detection]], [[continuous production]], and [[bioinformatics]]. In contrast with [[sequence mining]], association rule learning typically does not consider the order of items either within a transaction or across transactions. The association rule algorithm itself consists of various parameters that can make it difficult for those without some expertise in data mining to execute, with many rules that are arduous to understand.<ref>{{Cite web|last=Garcia|first=Enrique|date=2007|title=Drawbacks and solutions of applying association rule mining in learning management systems|url=https://sci2s.ugr.es/keel/pdf/specific/congreso/3-associationrules-Final.pdf|url-status=live|website=Sci2s|archive-url=https://web.archive.org/web/20091223124403/http://sci2s.ugr.es/keel/pdf/specific/congreso/3-associationrules-Final.pdf |archive-date=2009-12-23 }}</ref>
Edit summary
(Briefly describe your changes)
By publishing changes, you agree to the
Terms of Use
, and you irrevocably agree to release your contribution under the
CC BY-SA 4.0 License
and the
GFDL
. You agree that a hyperlink or URL is sufficient attribution under the Creative Commons license.
Cancel
Editing help
(opens in new window)