The Impact of Different Data Mining Classification Techniques in Different Datasets

Abstract

Data Mining is the process of finding knowledge through the processing of massive amounts of data from different viewpoints and combining them into valuable information; data mining has been a crucial part in various aspects of human life. It is used to recognize the covered up patterns in a huge amount of data. Classification methods are supervised learning methods that categorize the data item into known categories. Creating classification models from an input dataset is one of the most beneficial techniques in data mining; these methods typically create models that are used to forecast future patterns in data. This work has been done to assess the effectiveness of different classifiers algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), J48, and Neural Network (NN), these algorithms were applied on several datasets to determine the performance of the algorithm. All techniques were used with 10-fold cross-validation in the machine learning platform WEKA. According to the study’s findings, no algorithm has consistently performed best for each dataset.