We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal...
Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard "certain&quo...
In this paper, we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns. Specifically, we show that the problem of ...
Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent clo...
Abstract-- In battery driven portable applications, the minimization of energy, average power, peak power, and peak power differential are equally important to improve reliability ...