TCRM: Diagnosing tuple inconsistency for granulized datasets

Citations of this article
Mendeley users who have this article in their library.
Get full text


Many approaches to the granulization have been presented for knowledge discovery. However, the inconsistent tuples that exist in granulized datasets are hardly ever revealed. In this paper, we developed a model, tuple consistency recognition model (TCRM) to help efficiently detect inconsistent tuples for datasets that are granulized. The main outputs of the developed model include explored inconsistent tuples and consumed processing time. We further conducted an empirical test where eighteen continuous real-life datasets granulized by the equal width interval technique that embedded S-plus histogram binning algorithm (SHBA) and largest binning size algorithm (LBSA) binning algorithms were diagnosed. Remarkable results: almost 40% of the granulized datasets contain inconsistent tuples and 22% have the amount of inconsistent tuples more than 20%. © 2002 Elsevier Science B.V. All rights reserved.




Wu, C. H. (2002). TCRM: Diagnosing tuple inconsistency for granulized datasets. Knowledge-Based Systems, 15(8), 507–514.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free