scholarly journals ATURAN REKOMENDASI BARANG MENGGUNAKAN MULTI LEVEL ASSOCIATION RULES MINING (ML-ARM)

Transmisi ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 49
Author(s):  
Zahra Arwananing Tyas
Keyword(s):  
Association Rules ◽  
Association Rules Mining ◽  
Multi Level ◽  
F Measure

Sistem rekomendasi dapat menghasilkan rekomendasi dengan berbagai cara dan menggunakan berbagai macam metode, salah satunya adalah memanfaatkan tumpukan kasus lama atau tumpukan data transaksi lama yang dapat menghasilkan informasi atau aturan dengan metode Association Rules Mining(ARM). Aturan terbentuk dengan metode multi level ARM dan menghasilkan 5 aturan yang akan dicocokkan dengan masukan pengguna. Saat aturan ditemukan cocok maka consequent dari aturan tersebut akan dijadikan hasil rekomendasi.  Hasil pengujian dari aturan yang terbentuk memiliki nilai akurasi 94,12% dan nilai precision, recall dan F-measure untuk sistem rekomendasi ini pada proses rekomendasi dengan aturan yaitu berturut 0,475; 0,513 dan 0,25.

Different Types of Association Rules Mining Review

2012 ◽  
Vol 241-244 ◽  
pp. 1589-1592
Author(s):  
Jun Tan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Weighted Association Rules ◽  
Application Systems ◽  
Different Types ◽  
Multi Level

In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.

scholarly journals Efektifitas Penggunaan Association Rules Mining dalam Personalisasi Website

2021 ◽  
Vol 6 (1) ◽  
pp. 59
Author(s):  
Edi Priyanto ◽  
Arief Hermawan ◽  
Rianto Rianto ◽  
Donny Avianto
Keyword(s):  
Association Rules ◽  
System Approach ◽  
Recommendation System ◽  
Relevant Information ◽  
The Internet ◽  
Association Rules Mining ◽  
Log Data ◽  
Web Access ◽  
The Right ◽  
F Measure

As the usage of the internet grows, more and more information is obtained, thus presenting challenges, especially for users and website owners. Website users often have difficulty finding products or services that are relevant to their needs caused by abundant amounts of products and services delivered on a website. Website owners often find it difficult to convey information about the right products and services to certain target users. Based on the problem given above, we can conclude that a recommendation system approach that can improve personalization on their website is needed. The recommendation system approach must be able to provide navigation on the website to make it more adaptive towards the interests and information needed by the user. This study uses Association Rules formed from Microsoft web access log data by finding visitor patterns based on frequently visited web site pages. From the results of the research conducted, the performance of the method used has a precision value of 0.896, 0.058 recall, and F-measure 0.104. Whereas the measurement of the accuracy value resulted in a performance recommendation of exactly 3%, an acceptable rate of 87%, and 10% incorrect. This research shows that the Association Rules method can increase the effectiveness of website personalization to provide relevant information recommendations for visitors. For further research, it can concentrate on improving existing methods thus website personalization becomes more adaptive.

Multi-Level Association Rules Mining Algorithm Based on Semantic Relativity

2011 ◽  
Vol 460-461 ◽  
pp. 363-368
Author(s):  
Lei Zhang ◽  
Zhi Chao Wang
Keyword(s):  
Association Rules ◽  
Domain Knowledge ◽  
Semantic Meaning ◽  
Association Rules Mining ◽  
Minimum Support ◽  
Mining Algorithm ◽  
Multi Level

Traditional multi-level association rules mining approaches are based only on database contents. The relations of items in itemset are considered rarely. It leads to generate a lot of meaningless itemsets. Aiming at the problem,multi-level association rules mining algorithm based on semantic relativity is proposed. Domain knowledge is described by Ontology. Every item is seen as a concept in Ontology. Semantic relativity is used to measure the semantic meaning of itemsets. Minimum support of itemset is set according to its length and semantic relativity. Semantic related minimum support with length-decrease is defined to filter meaningless itemsets. Experiments results showed that the method in the paper can improve the efficiency of multi-level association rules mining and generated meaningful rules.

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