Optimizing Association Rule Mining Using Walk Back Artificial Bee Colony (WalkBackABC) Algorithm

2019 ◽  
pp. 39-48
Author(s):  
Imran Qureshi ◽  
Burhanuddin Mohammad ◽  
Mohammed Abdul Habeeb
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Rule Mining ◽  
Bee Colony

Artificial Bee Colony-Based Associative Classifier for Healthcare Data Diagnosis

2021 ◽  
pp. 237-253
Author(s):  
M. Nandhini ◽  
S. N. Sivanandam ◽  
S. Renugadevi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Optimization Technique ◽  
Rule Mining ◽  
Healthcare Data ◽  
Bee Colony ◽  
Small Set ◽  
Significant Class

Data mining is likely to explore hidden patterns from the huge quantity of data and provides a way of analyzing and categorizing the data. Associative classification (AC) is an integration of two data mining tasks, association rule mining, and classification which is used to classify the unknown data. Though association rule mining techniques are successfully utilized to construct classifiers, it lacks in generating a small set of significant class association rules (CARs) to build an accurate associative classifier. In this work, an attempt is made to generate significant CARs using Artificial Bee Colony (ABC) algorithm, an optimization technique to construct an efficient associative classifier. Associative classifier, thus built using ABC discovered CARs achieve high prognostic accurateness and interestingness value. Promising results were provided by the ABC based AC when experiments were conducted using health care datasets from the UCI machine learning repository.

Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony

2020 ◽  
Vol 144 ◽  
pp. 113097
Author(s):  
Akbar Telikani ◽  
Amir H. Gandomi ◽  
Asadollah Shahbahrami ◽  
Mohammad Naderi Dehkordi
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Artificial Bee Colony ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Bee Colony

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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