Maintenance of multi-level association rules discovery in dynamic database under a change of support threshold

2015 ◽  
Author(s):  
Nophadon Pumjun ◽  
Worapoj Kreesuradej
Keyword(s):  
Association Rules ◽  
Change Of Support ◽  
Support Threshold ◽  
Multi Level ◽  
Association Rules Discovery

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

2021 ◽  
Vol 30 (04) ◽  
pp. 2150018
Author(s):  
Anindita Borah ◽  
Bhabesh Nath
Keyword(s):  
Air Pollution ◽  
Association Rules ◽  
Air Pollutants ◽  
Pattern Mining ◽  
Environmental Parameters ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rare Association ◽  
Support Threshold ◽  
Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

Mining texts by association rules discovery in a technical corpus

2004 ◽  
pp. 89-98
Author(s):  
Mathieu Roche ◽  
Jérôme Azé ◽  
Oriane Matte-Tailliez ◽  
Yves Kodratoff
Keyword(s):  
Association Rules ◽  
Association Rules Discovery

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.

Closed-Itemset Incremental-Mining Problem

2011 ◽  
pp. 150-153
Author(s):  
Luminita Dumitriu
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Incremental Mining ◽  
Minimum Support ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

scholarly journals Inducing Multi-Level Association Rules from Multiple Relations

2004 ◽  
Vol 55 (2) ◽  
pp. 175-210 ◽  
Author(s):  
Francesca A. Lisi ◽  
Donato Malerba
Keyword(s):  
Association Rules ◽  
Multi Level
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