Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Baysen classifiers but decision tree mining is simple one.

ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees.

C4.5 is the Improved version on ID 3 . The new features (versus ID3) are: (i) accepts both continuous and discrete features; (ii) handles incomplete data points; (iii) solves over-fitting problem by (very clever) bottom-up technique usually known as "pruning"; and (iv) different weights can be applied the features that comprise the training data.

The next evolution of this is C5.0.

You might like to check this web article: https://dzone.com/articles/decision-trees-and-pruning-in-r

rPart is based in the CART implementation (according to the documentation). For C4.5, you could use the J4.8 (the c4.5 implementation in Java inside the weka toolkit) algorithm in rRweka package (https://CRAN.R-project.org/package=RWeka) and for C5.0, you could use C50 package (https://CRAN.R-project.org/package=C50)

Thank you all for your response.

Thank you so much manuel, the link describes exactly what i am looking for.

Thank you Ajit & Ronaldo for the valuable information.

Best Regards,

Lubn