document: TCRM : diagnosing tuple inconsistency for granulized datasets Many approaches to granularization have been presented for knowledge discovery . However , the inconsistent tuples that exist in granulized datasets are hardly ever revealed . We developed a model , tuple consistency recognition model -LRB- TCRM -RRB- 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 -LRB- SHBA -RRB- and largest binning size algorithm -LRB- LBSA -RRB- 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 %
doc_bio_tags: B O O B I O B I O O O B O O O O B I O O O O O O O O O B I O O O O O O O O O O B I I I O B O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I I I O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O
extractive_keyphrases: tcrm tuple inconsistency granulized datasets granularization knowledge discovery tuple consistency recognition model processing time equal width interval technique largest binning size algorithm
abstractive_keyphrases: relational database large database s-plus histogram binning algorithm sql