Editing Association rule learning (section)

== Other types of association rule mining ==

'''Multi-Relation Association Rules (MRAR)''': These are association rules where each item may have several relations. These relations indicate indirect relationships between the entities. Consider the following MRAR where the first item consists of three relations ''live in'', ''nearby'' and ''humid'': “Those who ''live in'' a place which is ''nearby'' a city with ''humid'' climate type and also are ''younger'' than 20 <math>\implies</math> their ''health condition'' is good”. Such association rules can be extracted from RDBMS data or semantic web data.<ref name="MRAR: Mining Multi-Relation Association Rules">Ramezani, Reza, Mohamad Saraee, and Mohammad Ali Nematbakhsh; ''MRAR: Mining Multi-Relation Association Rules'', Journal of Computing and Security, 1, no. 2 (2014)</ref>

'''[[Contrast set learning]]''' is a form of associative learning. '''Contrast set learners''' use rules that differ meaningfully in their distribution across subsets.<ref name="webb03">{{cite conference
 | author = GI Webb and S. Butler and D. Newlands
 | year = 2003
 | title = On Detecting Differences Between Groups
 | conference = KDD'03 Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
| url= http://portal.acm.org/citation.cfm?id=956781
 }}
</ref><ref name="busy">{{cite journal |doi=10.1109/MC.2003.1244531 |title=Computing practices - Data mining for very busy people |journal=Computer |volume=36 |issue=11 |pages=22–29 |year=2003 |last1=Menzies |first1=T. |last2=Ying Hu }}</ref>

'''Weighted class learning''' is another form of associative learning where weights may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results.

'''High-order pattern discovery''' facilitates the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real-world data.
<ref name="discovere">{{cite journal |doi=10.1109/69.649314 |title=High-order pattern discovery from discrete-valued data |journal=IEEE Transactions on Knowledge and Data Engineering |volume=9 |issue=6 |pages=877–893 |year=1997 |last1=Wong |first1=A.K.C. |last2=Yang Wang |citeseerx=10.1.1.189.1704 }}</ref>

'''[[K-optimal pattern discovery]]''' provides an alternative to the standard approach to association rule learning which requires that each pattern appear frequently in the data.

'''Approximate Frequent Itemset''' mining is a relaxed version of Frequent Itemset mining that allows some of the items in some of the rows to be 0.<ref>{{cite book |doi=10.1137/1.9781611972764.36 |chapter=Mining Approximate Frequent Itemsets in the Presence of Noise: Algorithm and Analysis |title=Proceedings of the 2006 SIAM International Conference on Data Mining |pages=407–418 |year=2006 |last1=Liu |first1=Jinze |last2=Paulsen |first2=Susan |last3=Sun |first3=Xing |last4=Wang |first4=Wei |last5=Nobel |first5=Andrew |last6=Prins |first6=Jan |isbn=978-0-89871-611-5 |citeseerx=10.1.1.215.3599 }}</ref>

'''Generalized Association Rules''' hierarchical taxonomy (concept hierarchy)

'''Quantitative Association Rules''' categorical and quantitative data

'''Interval Data Association Rules''' e.g. partition the age into 5-year-increment ranged

'''[[Sequential pattern mining]] ''' discovers subsequences that are common to more than minsup (minimum support threshold) sequences in a sequence database, where minsup is set by the user. A sequence is an ordered list of transactions.<ref name="sequence">Zaki, Mohammed J. (2001); ''SPADE: An Efficient Algorithm for Mining Frequent Sequences'', Machine Learning Journal, 42, pp. 31–60</ref>

'''Subspace Clustering''', a specific type of [[clustering high-dimensional data]], is in many variants also based on the downward-closure property for specific clustering models.<ref name="ZimekAssent2014">{{cite book|last1=Zimek|first1=Arthur|title=Frequent Pattern Mining|last2=Assent|first2=Ira|last3=Vreeken|first3=Jilles|year=2014|pages=403–423|doi=10.1007/978-3-319-07821-2_16|isbn=978-3-319-07820-5}}</ref>

'''Warmr''', shipped as part of the ACE data mining suite, allows association rule learning for first order relational rules.<ref>{{cite journal | pmid = 11272703 | volume=15 | issue=2 | title=Warmr: a data mining tool for chemical data. | date=Feb 2001 | journal=J Comput Aided Mol Des | pages=173–81| last1=King | first1=R. D. | last2=Srinivasan | first2=A. | last3=Dehaspe | first3=L. | bibcode=2001JCAMD..15..173K | doi=10.1023/A:1008171016861 | s2cid=3055046 }}</ref>