scholarly journals A Brief Overview of Swarm Intelligence-Based Algorithms for Numerical Association Rule Mining

2021 ◽  
pp. 47-59
Author(s):  
Iztok Fister ◽  
Iztok Fister
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

scholarly journals Shared Variable Extraction and Hardware Implementation for Nonlinear Boolean Functions Based on Swarm Intelligence

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Longmei Nan ◽  
Xiaoyang Zeng ◽  
Yiran Du ◽  
Zibin Dai ◽  
Lin Chen
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Boolean Functions ◽  
Association Rule Mining ◽  
Operation Time ◽  
Lookup Table ◽  
Coarse Grained ◽  
Rule Mining ◽  
Shared Variable ◽  
Shared Variables

To solve the problem of complex relationships among variables and the difficulty of extracting shared variables from nonlinear Boolean functions (NLBFs), an association logic model of variables is established using the classical Apriori rule mining algorithm and the association analysis launched during shared variable extraction (SVE). This work transforms the SVE problem into a traveling salesman problem (TSP) and proposes an SVE based on particle swarm optimization (SVE-PSO) method that combines the association rule mining method with swarm intelligence to improve the efficiency of SVE. Then, according to the shared variables extracted from various NLBFs, the distribution of the shared variables is created, and two corresponding hardware circuits, Element A and Element B, based on cascade lookup table (LUT) structures are proposed to process the various NLBFs. Experimental results show that the performance of SVE via SVE-PSO method is significantly more efficient than the classical association rule mining algorithms. The ratio of the rules is 80.41%, but the operation time is only 21.47% when compared to the Apriori method, which uses 200 iterations. In addition, the area utilizations of Element A and Element B expended by the NLBFs via different parallelisms are measured and compared with other methods. The results show that the integrative performances of Element A and Element B are significantly better than those of other methods. The proposed SVE-PSO method and two cascade LUT-structure circuits can be widely used in coarse-grained reconfigurable cryptogrammic processors, or in application-specific instruction-set cryptogrammic processors, to advance the performance of NLBF processing and mapping.

GPU-based swarm intelligence for Association Rule Mining in big databases

2019 ◽  
Vol 23 (1) ◽  
pp. 57-76 ◽  
Author(s):  
Youcef Djenouri ◽  
Philippe Fournier-Viger ◽  
Jerry Chun-Wei Lin ◽  
Djamel Djenouri ◽  
Asma Belhadi
Keyword(s):  
Swarm Intelligence ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

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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