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A decision tree is a popular data mining technique that is widely used for analysis of road accident data.
Decision tree J48 and CAR which are the most popular data mining methods are used to generate a predictive model and to find the appropriate rules.
Association rule mining is a very popular data mining technique based on market basket analysis that extracts interesting rules between various attributes in a large data set [18].
Decision tree classification algorithm is a popular data mining classifier for prediction due to the ease of understating and the interaction between variables.
Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident.
Rule mining is one of the most popular data mining tools used for such purposes due to its simplicity and efficiency.
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In this study, 11 popular data-mining methods (ANN, kNN, J48, LgBoost, LRT, Logistic, NaiveB, RF, RSSpace, SMO and Vote) were trained and compared to the baseline ZeroR model to identify the predictability power of the built models.
In particular, online analytical processing (OLAP) and decision support systems (DSS) have become popular for data mining and business analysis.
Comparative studies will be carried out to compare the performance of functional networks with the most popular existing data mining techniques, such as, statistical regression multilayer feed forward neural networks, and support vector machines.
Mining association rules is very popular in the data mining community.
k-means is one of the most popular algorithms in data mining and can be easily adapted to the MapReduce model.
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