Decision Tree Analysis on J48 and Random Forest Algorithm for Data Mining Using Breast Cancer Microarray Dataset

Data mining which involves systematic analyses of large datasets for extracting the knowledge. Classification is considered as one of the major basic research topics that manage the data. Due to the rapid developments in microarray technology and it offer the capability to measure expression levels of thousands of genes simultaneously. study of such data helps us discovering different clinical outcomes that are caused by expression of a few predictive genes. Decision tree models help in predicting new data. In this work, we make a comparison of Decision Tree algorithms. We use two most popular algorithms namely basically J48 and Random Forest using Breast cancer microarray dataset which is available at UCI machine learning repository.