A proposal on analysis support system based on association rule analysis for non-dominated solutions

2014 ◽  
Author(s):  
Shinya Watanabe ◽  
Yuta Chiba ◽  
Masahiro Kanazaki
Keyword(s):  
Support System ◽  
Association Rule ◽  
Rule Analysis

Mining Co-Occurrence Patterns among Deep Road Distresses Using Association Rule Analysis

2022 ◽  
Vol 148 (1) ◽  
Author(s):  
Qian Gao ◽  
Chenglong Liu ◽  
Yishun Li ◽  
Yuchuan Du ◽  
Guanghua Yue ◽  
...  
Keyword(s):  
Association Rule ◽  
Rule Analysis ◽  
Occurrence Patterns

Monthly Precipitation Forecast of Jiulong River Basin Based on Association Rule Analysis

2021 ◽  
Author(s):  
Linjiang Nan ◽  
Mingxiang Yang ◽  
Jianqiu Li ◽  
Ningpeng Dong ◽  
Hejia Wang
Keyword(s):  
River Basin ◽  
Association Rule ◽  
Precipitation Forecast ◽  
Monthly Precipitation ◽  
Jiulong River ◽  
Rule Analysis

An Association Rule Analysis Framework for Complex Physiological and Genetic Data

2012 ◽  
pp. 131-142 ◽  
Author(s):  
Jing He ◽  
Yanchun Zhang ◽  
Guangyan Huang ◽  
Yefei Xin ◽  
Xiaohui Liu ◽  
...  
Keyword(s):  
Association Rule ◽  
Genetic Data ◽  
Analysis Framework ◽  
Rule Analysis

Human resource performance evaluation from CRM perspective: a two-step association rule analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 89 ◽  
Author(s):  
Elham Akhondzadeh Noughabi ◽  
Mohammad Reza Amin Naseri ◽  
Amir Albadvi ◽  
Mohammad Saeedi
Keyword(s):  
Performance Evaluation ◽  
Human Resource ◽  
Association Rule ◽  
Rule Analysis

A fuzzy inference method based on association rule analysis with application to river flood forecasting

2012 ◽  
Vol 66 (10) ◽  
pp. 2090-2098 ◽  
Author(s):  
Chi Zhang ◽  
Yilun Wang ◽  
Lili Zhang ◽  
Huicheng Zhou
Keyword(s):  
Association Rule ◽  
Fuzzy Inference ◽  
Fuzzy Reasoning ◽  
Flood Forecasting ◽  
Fuzzy Rules ◽  
Computationally Efficient ◽  
River Flood ◽  
Input Parameters ◽  
Rule Analysis ◽  
Takagi Sugeno

In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum–Cunge scheme.

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