Multidrug resistance analysis method for pathogens of cow mastitis based on weighted-association rule mining and similarity comparison

2021 ◽  
Vol 190 ◽  
pp. 106411
Author(s):  
Buwen Liang ◽  
Xinxing Li ◽  
Ziyi Zhang ◽  
Congming Wu ◽  
Xin Liu ◽  
...  
Keyword(s):  
Multidrug Resistance ◽  
Association Rule ◽  
Association Rule Mining ◽  
Analysis Method ◽  
Rule Mining ◽  
Similarity Comparison ◽  
Resistance Analysis

scholarly journals A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit

2021 ◽  
Vol 11 (11) ◽  
pp. 5221
Author(s):  
Xuecen Zhang ◽  
Yi Tang ◽  
Qiang Liu ◽  
Guofeng Liu ◽  
Xin Ning ◽  
...  
Keyword(s):  
Power Systems ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distribution Networks ◽  
Fault Analysis ◽  
Analysis Method ◽  
Key Factors ◽  
Rule Mining ◽  
Safe Operation ◽  
Main Concept

With the development of distribution networks, large amounts of distribution terminal units (DTU) are gradually integrated into the power system. However, limited numbers of maintenance engineers can hardly cope with the pressure brought about by the substantial increase of DTU devices. As DTU fault would pose a threat to the stable and safe operation of power systems; thus, it is rather significant to reduce the fault incidence of DTU devices and improve the efficiency of fault elimination. In this paper, a DTU fault analysis method using an association rule mining algorithm was proposed. Key factors of DTU fault were analyzed at first. Then, the main concept of the Eclat algorithm was illustrated, and its performance was compared with FP-growth and Apriori algorithms using DTU fault databases of different sizes. Afterwards, a DTU fault analysis method based on the Eclat algorithm was proposed. The practicality of this method was proven by experiment using a realistic DTU fault database. Finally, the application of this method was presented to demonstrate its effectiveness.

scholarly journals Corrigendum: Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

2020 ◽  
Vol 11 ◽  
Author(s):  
Casey L. Cazer ◽  
Mohammad A. Al-Mamun ◽  
Karun Kaniyamattam ◽  
William J. Love ◽  
James G. Booth ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Multidrug Resistance ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Resistance Patterns

scholarly journals Shared Multidrug Resistance Patterns in Chicken-Associated Escherichia coli Identified by Association Rule Mining

2019 ◽  
Vol 10 ◽  
Author(s):  
Casey L. Cazer ◽  
Mohammad A. Al-Mamun ◽  
Karun Kaniyamattam ◽  
William J. Love ◽  
James G. Booth ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Multidrug Resistance ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Resistance Patterns

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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