C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
The utility of feature construction for back-propagation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Planning of educational training courses by data mining: Using China Motor Corporation as an example
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Decision trees: a recent overview
Artificial Intelligence Review
| Hi-index | 12.05 |
Data Mining has been successful in a wide variety of application areas for varied purposes. Data Mining itself is done using several different methods. Decision Trees are one of the popular methods that have been used for Data Mining purposes. Since the process of constructing these decision trees assume no distributional patterns in the data (non-parametric), characteristics of the input data are usually not given much attention. We consider some characteristics of input data and their effect on the learning performance of decision trees. Preliminary results indicate that the performance of decision trees can be improved with minor modifications of input data.