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High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining high-utility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present...
Frequent itemset mining (FIM) is a fundamental research topic, which consists of discovering useful and meaningful relationships between items in transaction databases. However, FIM suffers from two important limitations. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate, imprecise, or incomplete...
In recent years, many algorithms have been proposed to discover frequent itemsets over uncertian databases or mine weighted-based frequent itemsets from precisely binary databases. None of the above algorithms have been, however, designed to discover interesting patterns by considering both weight and data uncertainty constraints. In this paper, a novel knowledge called high expected weighted itemsets...
In this paper, we propose an efficient projection-based algorithm to discover high sequential utility patterns from quantitative sequence databases. An effective pruning strategy in the proposed algorithm is designed to tighten upper-bounds for subsequences in mining. By using the strategy, a large number of unpromising subsequences could be pruned to improve execution efficiency. Finally, the experimental...
In this paper, we try to improve the performance of utility mining. We propose a new projection-based mining algorithm and embed two pruning strategies in it to efficiently find high utility itemsets in a database. By using the two designed strategies, a large number of unpromising itemsets can be pruned away at an early stage, and the data size could recursively be reduced to save the scan time....
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