Constructing a multi-valued and multi-labeled decision tree

Expert Systems with Applications

Volumn 25, Issue 2, 2003, Pages 199-209

  Chen, Yen Liang ^a   Hsu, Chang Ling ^a   Chou, Shih Chieh ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

Author keywords

Classification; Customer relation management; Data mining; Decision tree; Multi labeled attribute; Multi valued attribute; Prediction

Indexed keywords

CUSTOMER SATISFACTION; DATA MINING; DATA REDUCTION; DECISION THEORY; FUZZY SETS;

DECISION TREE;

EXPERT SYSTEMS;

EID: 0037507339     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0957-4174(03)00047-2     Document Type: Article

References (19)

1. Agrawal, R., Ghosh, S., Imielinski, T., Iyer, B., & Swami, A (1992). An interval classifier for database mining applications. Proceedings of the 18th International Conference on Very Large Databases (pp. 560-573). Vancouver, BC.
2. Agrawal R., Imielinski T., Swami A. Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering. 5:(6):1993;914-925.
3. Bramer M. Using J-pruning to reduce overfitting in classification trees. Knowledge-Based Systems. 15:(5/6):2002;301-308.
4. Chaudhuri S., Dayal U. An overview of data warehousing and OLAP technology. ACM SIGMOD Record. 26:(1):1997;65-74.
5. Codd E.F. Further normalization of the data base relational model. Data base systems, courant computer science symposium series 6. 1972;Prentice-Hall, Englewood Cliffs, NJ. pp. 33-64.
6. Han, J., Cai, Y., & Cercone, N (1992). Knowledge discovery in databases: an attribute-oriented approach. Proceedings of International Conference on Very Large Databases (pp. 547-559). Vancouver, Canada.
7. Han J., Kamber M. Data mining: Concepts and techniques. 2001;Morgan Kaufmann, San Francisco, CA.
8. Han J., Nishio S., Kawano H., Wang W. Generalization-based data mining in object-oriented databases using an object-cube model. Data and Knowledge Engineering. 25:(1/2):1998;55-97.
9. Mehta, M., Agrawal, R., & Rissanen, J (1996). SLIQ: A fast scalable classifier for data mining. Proceedings of the Fifth International Conference on Extending Database Technology (pp. 18-32). Avigon, France.
10. Moshkovich H.M., Mechitov A.I., Olson D.L. Rule induction in data mining: effect of ordinal scales. Expert Systems with Applications. 22:(4):2002;303-311.
11. Quinlan J.R. Induction of decision trees. Machine Learning. 1:(1):1986;81-106.
12. Quinlan J.R. C4.5: Programs for machine learning. 1993;Morgan Kaufmann, San Mateo, CA.
13. Rastogi R. PUBLIC: a decision tree classifier that integrates building and pruning. Data Mining and Knowledge Discovery. 4:(4):2000;315-344.
14. Ruggieri S. Efficient C4.5. IEEE Transactions on Knowledge and Data Engineering. 14:(2):2002;438-444.
15. Shafer, J. C., Agrawal, R., & Mehta, M (1996). SPRINT: A scalable parallel classifier for data mining. Proceedings of the 22nd International Conference on Very Large Databases (pp. 544-555). Mumbai (Bombay), India.
16. Steinberg D., Colla P.L. CART: Tree-structured nonparametric data analysis. 1995;Salford Systems, San Diego, CA.
17. Umano, M., Okamoto, H., Hatono, I., Tamura, H., Kawachi, F., Umedzu, S., & Kinoshita, J (1994). Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems. Proceedings of the third IEEE International Conference on Fuzzy Systems, Vol. 3 (pp. 2113-2118). Orlando, FL.
18. Wang, M., Iyer, B., & Vitter, J. S (1998). Scalable mining for classification rules in relational databases. Proceedings of International Database Engineering and Applications Symposium (pp. 58-67). Cardiff, Wales, UK.
19. Wang, H., & Zaniolo, C (2000). CMP: A fast decision tree classifier using multivariate predictions. Proceedings of the 16th International Conference on Data Engineering (pp. 449-460).