Classical association rule mining algorithm discovers frequent itemsets from transactional databases by considering the appearance of the itemset and not other utilities such as profit of an item or quantity in which items bought. But in transactional databases large quantity of items is purchased may lead to very high profit even though items appeared in few transactions. Therefore the quantit...

We show that a form of synaptic plasticity recently discovered in slices of the rat visual cortex (Artola et al. 1990) can support an error-correcting learning rule. The rule increases weights when both preand postsynaptic units are highly active, and decreases them when pre-synaptic activity is high and postsynaptic activation is less than the threshold for weight increment but greater than a ...

In this paper, we propose the use of weighted linguistic fuzzy rules in combination with a rule selection process to develop accurate fuzzy logic controllers dedicated to the intelligent control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. To do so, a genetic optimization process considering an efficient approach to perform ...

We give a positive equivariant Littlewood-Richardson rule also discovered independently by Molev. Our proof generalizes a proof by Stembridge of the ordinary Littlewood-Richardson rule. We describe a weight-preserving bijection between our indexing tableaux and the Knutson-Tao puzzles.

We tested the prediction that global warming has caused recent decreases in body weight (Bergmann’s rule) and increases in wing length (Allen’s rule) in 14 species of passerine birds at two localities in England: Wicken Fen (1968 /2003) and Treswell Wood (1973 /2003). Predicted long-term linear decreases in residual body weight occurred in four species: dunnocks (Wicken Fen), and great tits, bl...

Association rule mining aims to explore large transaction databases for association rules. Classical Association Rule Mining (ARM) model assumes that all items have the same significance without taking their weight into account. It also ignores the difference between the transactions and importance of each and every itemsets. But, the Weighted Association Rule Mining (WARM) does not work on dat...

Children learn about the social and physical world by observing other people's acts. This experiment tests both Chinese and American children's learning of a rule. For theoretical reasons we chose the rule of categorizing objects by the weight. Children, age 4 years, saw an adult heft four visually-identical objects and sort them into two bins based on an invisible property-the object's weight....

We give a positive equivariant Littlewood-Richardson rule also discovered independently by Molev. Our proof generalizes a proof by Stembridge of the ordinary Littlewood-Richardson rule. We describe a weight-preserving bijection between our indexing tableaux and the Knutson-Tao puzzles.

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....