SOTARM: Size of transaction-based association rule mining algorithm

Asha PANDIAN; Jebarajan THAVEETHU

Makale detayları

SOTARM: Size of transaction-based association rule mining algorithm

Yazarlar:

Asha PANDIAN | Jebarajan THAVEETHU

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 1, pp.278-291

Belge Tipi:

Makale

Yayın Tarihi:

1/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000741223

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Mining of association rules tries to identify the   existence of promising and fruitful relations among the items present in a   database. The basic a priori algorithm suffers from multiple database scans,   and if the database is large, then the time taken for scanning and   generation of candidates is also large. The proposed algorithm attempts to   reduce the repeated scanning of the whole database. Using this algorithm,   scanning time and also the generation of subitems that are not frequent can   be reduced. The former can be done by sorting the transaction records in   descending order based on the size of transaction (SOT) and scanning only   those transactions whose SOT is greater than or equal to k (size of item   sets). The latter can be done by analyzing the item set state. It is not   required to generate the next set of candidate item sets using those item   sets that are not frequent. Both positive and negative mining has been done   in R Studio of the R data mining tool using the R language. Experimental   results show that the SOT algorithm performs better than the Apriori, Eclat,   PVARM (partition-based validation for association rule mining), and NRRM   (nonredundant rule method) algorithms. The work has been tested against   various standard datasets such as Adult, Genome, Groceries, and SER (State   Electricity Rate) Prediction. The speed-up and efficiency parameter values   obtained from the algorithm strongly suggest that the proposed SOTARM   algorithm has attained better performance when compared to all the other   existing algorithms.

Makale detayları

SOTARM: Size of transaction-based association rule mining algorithm

Yazarlar: Asha PANDIAN | Jebarajan THAVEETHU

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 1, pp.278-291

Belge Tipi: Makale

Yayın Tarihi: 1/2017

Belge Dili: Türkçe

Document ID (Org No.): 2000741223

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Asha PANDIAN | Jebarajan THAVEETHU

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2017 Vol.25 Issue 1, pp.278-291

Belge Tipi:

Makale

Yayın Tarihi:

1/2017

Belge Dili:

Türkçe

Document ID (Org No.):

2000741223

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632