Document Server@UHasselt >
School for Information Technology >
Master theses >
Please use this identifier to cite or link to this item:
|Title: ||Mining of frequent sets using pruning, based on background knowledge|
|Authors: ||JAGER, Anke|
|Advisors: ||KUIJPERS, B.|
|Issue Date: ||2007|
|Abstract: ||Association rule mining is a technique to find useful patterns and associations in transactional
databases. There have been developed a lot of algorithms for this purpose, among which
are also APriori and FP-Growth. Though you can find new patterns and associations, the
technique of association rule mining usually results in too many rules through which the user
has to find those that are interesting to him/her. Among this large amount of association
rules, there are also ones that are non-interesting, simply because they are known a priori,
like for example isPregnant ! isFemale.
Since these rules are not useful, their frequent itemsets also do not need to be generated.
This method was already described in [Bog06], where the idea of knowledge constraints was
applied to APriori. Because the FP-Growth algorithm is a lot faster than APriori, it seems
logical to also apply this method to FP-Growth.
The only drawback for this new algorithm (and also for APriori-KC) was that there were
removed too many rules through this elimination of dependences. Therefore, we developed a
method to recover the rules that were lost, without too much time going lost.
The main advantage of this new algorithm is that it reduces the number of frequent
itemsets significantly and thus also the total number of association rules that is generated.|
|Notes: ||Master in de Informatica - Databases|
|Type: ||Theses and Dissertations|
|Appears in Collections: ||Master theses|
Files in This Item:
|N/A||3.07 MB||Adobe PDF|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.