Soft set based association rule mining

2016 ◽  
Vol 111 ◽  
pp. 268-282 ◽  
Author(s):  
Feng Feng ◽  
Junghoo Cho ◽  
Witold Pedrycz ◽  
Hamido Fujita ◽  
Tutut Herawan
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Soft Set ◽  
Rule Mining

Usage of Fuzzy, Rough, and Soft Set Approach in Association Rule Mining

2012 ◽  
Vol 3 (3) ◽  
pp. 64-77 ◽  
Author(s):  
Satya Ranjan Dash ◽  
Satchidananda Dehuri ◽  
Uma kant Sahoo
Keyword(s):  
Set Theory ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Soft Set ◽  
Rule Mining

This paper is two folded. In first fold, the authors have illustrated the interplay among fuzzy, rough, and soft set theory and their way of handling vagueness. In second fold, the authors have studied their individual strengths to discover association rules. The performance of these three approaches in discovering comprehensible rules are presented.

Interactions and Applications of Fuzzy, Rough, and Soft Set in Data Mining

2013 ◽  
Vol 3 (3) ◽  
pp. 37-50
Author(s):  
Satya Ranjan Dash ◽  
Satchidananda Dehuri ◽  
Uma kant Sahoo
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Practical Importance ◽  
Soft Set ◽  
Knowledge Discovery In Databases ◽  
Rule Mining ◽  
Societal Problem

In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more attention from both theoretical and practical side. Therefore, to make some improvement in this direction, the authors studied soft set theory and its interaction with fuzzy and rough set. Alongside, the authors have taken a numerical example related to a societal problem for realizing the practical importance of these theories.

A Novel Market Basket Analysis Using Adaptive Association Rule Mining Algorithm

2012 ◽  
Vol 1 (4) ◽  
pp. 25-28
Author(s):  
M.Dhanabhakyam M.Dhanabhakyam ◽  
◽  
Dr.M.Punithavalli Dr.M.Punithavalli
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Market Basket Analysis ◽  
Rule Mining ◽  
Market Basket ◽  
Mining Algorithm

Prediksi Code Defect Perangkat Lunak Dengan Metode Association Rule Mining dan Cumulative Support Thresholds

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Rizal Setya Perdana ◽  
Umi Laili Yuhana
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Program Code

Kualitas perangkat lunak merupakan salah satu penelitian pada bidangrekayasa perangkat lunak yang memiliki peranan yang cukup besar dalamterbangunnya sistem perangkat lunak yang berkualitas baik. Prediksi defectperangkat lunak yang disebabkan karena terdapat penyimpangan dari prosesspesifikasi atau sesuatu yang mungkin menyebabkan kegagalan dalam operasionaltelah lebih dari 30 tahun menjadi topik riset penelitian. Makalah ini akandifokuskan pada prediksi defect yang terjadi pada kode program (code defect).Metode penanganan permasalahan defect pada kode program akan memanfaatkanpola-pola kode perangkat lunak yang berpotensi menimbulkan defect pada data setNASA untuk memprediksi defect. Metode yang digunakan dalam pencarian polaadalah memanfaatkan Association Rule Mining dengan Cumulative SupportThresholds yang secara otomatis menghasilkan nilai support dan nilai confidencepaling optimal tanpa membutuhkan masukan dari pengguna. Hasil pengujian darihasil pemrediksian defect kode perangkat lunak secara otomatis memiliki nilaiakurasi 82,35%.

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