On changing continuous attributes into ordered discrete attributes
EWSL-91 Proceedings of the European working session on learning on Machine learning
Automated knowledge acquisition
Automated knowledge acquisition
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
A database perspective on knowledge discovery
Communications of the ACM
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
DBMiner: interactive mining of multiple-level knowledge in relational databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining and KDD: promise and challenges
Future Generation Computer Systems - Special double issue on data mining
Introduction to S and S-Plus
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
Applied Intelligence
An Extension to SQL for Mining Association Rules
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Induction By Attribute Elimination
IEEE Transactions on Knowledge and Data Engineering
Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
A new extension of fuzzy sets using rough sets: R-fuzzy sets
Information Sciences: an International Journal
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
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Knowledge discovery from databases requires comprehensive pre-examination to ensure that granulated datasets are consistent for continuous database conversion. Different granulation techniques may produce different results in the number of conflicting data in a granulated dataset. This work examines and compares the performance of equal width interval (EWI) and equal frequency interval (EFI), two granulation techniques. This work also explores the relationship between granulation performance and dataset size, number of attributes, and number of classes. Eighteen continuous datasets are examined. Experimental results indicate that (1) of the 18 datasets examined, 7 contained conflicting data by EWI and 8 by EFI, suggesting that almost 40% of the granulated datasets contained conflicting data; (2) almost 22% of the datasets had more than 20% conflicting data; (3) comparatively, no notable difference existed between EWI and EFI with respect to their granulation performance; (4) the production of conflicting data by EWI and EFI when compared against dataset size and number of classes was not remarkably different; and (5) more than 12 attributes will reduce the number of conflicting data by both EWI and EFI.