A novel data mining approach to transient stability assessment based on associative classification

2003 ◽  
Author(s):  
Z.H. Yu
Keyword(s):  
Data Mining ◽  
Transient Stability ◽  
Stability Assessment ◽  
Associative Classification ◽  
Data Mining Approach

MAC: A Multiclass Associative Classification Algorithm

2012 ◽  
Vol 11 (02) ◽  
pp. 1250011 ◽  
Author(s):  
Neda Abdelhamid ◽  
Aladdin Ayesh ◽  
Fadi Thabtah ◽  
Samad Ahmadi ◽  
Wael Hadi
Keyword(s):  
Data Mining ◽  
Rule Induction ◽  
Classification Systems ◽  
Data Repository ◽  
Traditional Learning ◽  
Data Sets ◽  
Learning Approaches ◽  
Associative Classification ◽  
Data Mining Approach ◽  
Association Rule Discovery

Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments.

Transient Stability Multi Swing Step-out Prediction with Online Data Mining

2016 ◽  
Vol 136 (2) ◽  
pp. 137-144 ◽  
Author(s):  
Takuya Omi ◽  
Hiroto Kakisaka ◽  
Shinichi Iwamoto
Keyword(s):  
Data Mining ◽  
Transient Stability ◽  
Online Data

A Data-Mining Approach for Energy Behavioural Analysis to Ease Predictive Modelling for the Smart City

2018 ◽  
Author(s):  
Lavinia Chiara Tagliabue ◽  
Stefano Rinaldi ◽  
Mario Favalli Ragusini ◽  
Giovanni Tardioli ◽  
Angelo Luigi Camillo Ciribini
Keyword(s):  
Data Mining ◽  
Smart City ◽  
Predictive Modelling ◽  
Data Mining Approach ◽  
Behavioural Analysis

Data mining approach to construction productivity prediction for cutter suction dredgers

2019 ◽  
Vol 105 ◽  
pp. 102833 ◽  
Author(s):  
Shuo Bai ◽  
Mingchao Li ◽  
Rui Kong ◽  
Shuai Han ◽  
Heng Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Construction Productivity ◽  
Data Mining Approach ◽  
Productivity Prediction
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