scholarly journals Pembentukan Temporal Association Rules Menggunakan Algoritma Apriori (Studi Kasus:Toko Batik Diyan Solo)

2016 ◽  
Vol 10 (1) ◽  
pp. 71
Author(s):  
Annisa Mauliani ◽  
Sri Hartati ◽  
Aina Musdholifah
Keyword(s):  
Decision Support ◽  
Association Rules ◽  
Temporal Association ◽  
Apriori Algorithm ◽  
Association Rules Mining

In this study the adding of time aspect on association rules mining result was used. The aspect of time that was used in this study was date of transactions. The information that was resulted from time aspect adding in association rules mining was known as temporal association rules mining..            In this study, Apriori Algorithm was used for the forming of temporal association rules. This result which show Algorithm Apriori and Temporal Association Rules can be used to get more information about Temporal Association Rules. The Result of this examination can be used for decision support for manager. Because the result of Temporal Association Rules have explanation about event. So from the result of Temporal Association Rules can be knowing, the Temporal Association Rules happened at the time of event.In Ramadan Idul Fitri 2013 and Ramadan Idul Fitri 2014 with parameter assess minsup 10%, mintempsup 5, minconf 50%, resulting different Temporal Association Rules. In Ramadan Idul Fitri 2013, no resulting Temporal Association Rules. While is on Ramadan Idul Fitri 2014 resulting Temporal Association Rules with the biggest value of support 14%, that is {BLBP} à  {HPCK}.

An Efficient Weighted Association Rules Mining Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1247-1250 ◽  
Author(s):  
Na Xin Peng
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Apriori Algorithm ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Mining Algorithm ◽  
Pruning Strategy

Aiming at the problem that most of weighted association rules algorithm have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, Boolean weighted association rules algorithm and weighted fuzzy association rules algorithm are presented, which use pruning strategy of Apriori algorithm so as to improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

A New Algorithm of Association Rules Mining Based on Relation Matrix

2011 ◽  
Vol 179-180 ◽  
pp. 55-59
Author(s):  
Ping Shui Wang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Automatic Extraction ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Research Areas ◽  
Mining Algorithms ◽  
Relation Matrix

Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.

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