DOI: 10.5176/978-981-08-7466-7_kd-22

Authors: Mariam Adedoyin-Olowe and Mohamed Medhat Gaber


This paper investigated the performance of four of the top rated classification techniques, namely, Naive Bayes, K Nearest Neighbour (K-NN), Decision Tree (ID3) and CART as stated and voted for in the Top 10 data mining algorithms in[20]. We analysed their performance on 10 real nominal data sets and discovered their strengths and weaknesses based on different features of the data sets. It could be seen using the produced results that different features of data sets would determine how fast and accurate the different classification techniques are. This study and the recommendations we provide at the end would help data mining practitioners on deciding the appropriate technique based on the dataset’s number of attributes, instances, class labels and the total size.

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