A new classification method to overcome over-branching
Journal of Computer Science and Technology
Ontology-Driven Induction of Decision Trees at Multiple Levels of Abstraction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Association Rules & Evolution in Time
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
An Integrated Classification Rule Management System for Data Mining
WAIM '01 Proceedings of the Second International Conference on Advances in Web-Age Information Management
DBMiner: a system for data mining in relational databases and data warehouses
CASCON '97 Proceedings of the 1997 conference of the Centre for Advanced Studies on Collaborative research
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Toward an optimum combination of English teachers for objective teaching
Expert Systems with Applications: An International Journal
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Statistics based predictive geo-spatial data mining: forest fire hazardous area mapping application
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
Database implementation of a model-free classifier
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
Multi-granularity classification rule discovery using ERID
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
On-the-fly generalization hierarchies for numerical attributes revisited
SDM'11 Proceedings of the 8th VLDB international conference on Secure data management
Performance evaluation of an agent based distributed data mining system
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
An Efficient Method for Discretizing Continuous Attributes
International Journal of Data Warehousing and Mining
| Hi-index | 0.00 |
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient, high quality, multiple level classification of large amounts of data, the relaxation of the requirement of perfect training sets, and the elegant handling of continuous and noisy data.