Descriptive and Prescriptive Analysis of Construction Site Incidents Using Decision Tree Classification and Association Rule Mining

2021 ◽  
Author(s):  
Ozgur Ugur ◽  
Ali Atilla Arisoy ◽  
Murat Can Ganiz ◽  
Berkay Bolac
Keyword(s):  
Decision Tree ◽  
Association Rule ◽  
Association Rule Mining ◽  
Construction Site ◽  
Rule Mining ◽  
Decision Tree Classification ◽  
Prescriptive Analysis

scholarly journals Implementation of Client Controlled Privacy Preserving Data Model for Mining Decision Rules using Decision Tree and Association Rules

2019 ◽  
Vol 9 (1) ◽  
pp. 3617-3624
Keyword(s):  
Decision Tree ◽  
Data Model ◽  
Association Rule ◽  
Association Rule Mining ◽  
Decision Rules ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Data Owner ◽  
Higher Dimensional ◽  
Near Future

The privacy-preserving data mining (PPDM) is one of the techniques which are used for mining data dynamically with preserving privacy of the end data owner. In this paper, a PPDM technique for generating the privacy-preserving decision rules is proposed and implemented. The key motive of presenting this privacy-preserving decision rule mining technique is to demonstrate how securely data is aggregated in the PPDM environment, how securely extract them and consumed them with the help of applications. In addition to comparing the state of art methods for mining privacy preserving decision rules for preparing the future directions of research. Therefore two different data models have used namely decision tree and association rule mining. The conducted experiments demonstrate that decision tree-based techniques are superior to the association rule mining based techniques for mining higher dimensional data with higher accuracy and low resource consumption. Therefore in the near future for extending this data model the two concepts are also introduced in this paper.

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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