Magesh Waran N


The main objective is to provide the performance appraisal report of an employee using Decision Tree algorithm. The data mining classification methods like decision tree, rule mining, clustering etc. can be applied for evaluating the employee data for giving promotion, yearly increment and career advancement. In order to provide yearly increment for an employee, the historical data stored in the table are subjected to learning by using the decision tree algorithm and the performance are found by testing the attributes of an employee against the rules generated by the decision tree classifier. This paper concentrates on collecting data about employees using the user interface, generating a decision tree from the historical data, testing the decision tree with attributes of an employee and generating the output as whether to give the promotion or not. The information of the user is compared with the trained data stored in the decision tree. The final goal node is to determine whether the employee will get yearly increment, promotion or not.


learning, training, testing, prediction, Decision tree, J48 algorithm

Full Text:



Anand Bahety Department of Computer Science University of Maryland, College Park “Extension and Evaluation of ID3 – Decision Tree Algorithm”

Brijesh Kumar, Baradwaj, Saurabh Pal “Mining Educational Data to Analyze Students Performance “ ,” IJACSA International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.

Ying Liu, et all , "Region-based image retrieval with high-level semantics using decision tree learning," Journal of Pattern Recognition, Vol. 41, No. 8, pp. 2554 – 2570, Aug 2008.

Breiman, Friedman, Olshen, and Stone. ― Classification and Regression Trees, Wadsworth, Mezzovico, Switzerland. 1984,

Daniel Rodríguez “Making predictions on new data using Weka” University of Alcala.

Matthew N.Anyanwu, Sajjan G.Shiva, ―Comparative Analysis of Serial Decision Tree Classification Algorithms, International Journal of Computer Science and Security, volume 3.

Mitra S, Acharya T. Data Mining.Multimedia, Soft Computing, and Bioinformatics. John Wiley & Sons, Inc., Hoboken, New Jersey; 2003.

Parr Rud, O. Data Mining Cookbook.Modeling Data for Marketing, Risk, and Customer Relationship Management. John Wiley & Sons, Inc.; 2001.

Quinlan, J.R. Induction of decision trees. Machine Learning, volume 1. Morgan Kaufmann; 1876. p. 71-96.

Quinlan, J.R., (1883), C4.5:Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann.

S.Anupama Kumar and Dr. M.N.Vijayalakshmi “Efficiency of Decision Trees in Predicting Student’s Academic Performance”.

S.Anupama Kumar, Dr.M.N.Vijayalakshmi,“A Novel Approach in Data Mining Techniques for Educational Data” , Proc 2011 3rd International Conference on Machine Learning and Computing” (ICMLC 2011) , Singapore, 26th-27th Feb 2011,pp V4-152-154.

Samrat Singh, Dr. Vikesh Kumar “Classification of Student’s data Using Data Mining Techniques for Training & Placement Department in Technical Education”.

Stuart Russell, Peter Norvig “Artificial Intelligence A Modern Approach” and Amos Storkey “Learning from Data: Decision Trees”, School of Informatics ,University of Edinburgh Semester 1, 2004.

Weka, University of Waikato, New Zealand,http://www.cs.waikato.ac.nz/ml/weka/

Yael Ben-Haim Elad Tom-Tov “A Streaming Parallel Decision Tree Algorithm”, IBM Haifa Research Lab, Haifa University Campus.

Farhad Soleimanian Gharehchopogh, Peyman Mohammadi, Parvin Hakimi “Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study”.

Erick Swere and David J Mulvaney, “Robot Navigation Using Decision Trees”.

Hang Yang, Simon Fong, Guangmin Sun, and Raymond Wong “A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network”.


  • There are currently no refbacks.