An Efficient Approach to Discovering Frequent Patterns from Data Cube using Aggregation and Directed Graph

High‐utility itemset (HUI) mining is an important tasks during data mining. Recently, many algorithms have been proposed to discover HUIs. Most of the algorithms work only for itemsets with positive utility values. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Efficient High‐utility Itemsets mining with Negative utility (EHIN) to find all HUIs with negative utility. EHIN utilises 2 new upper bounds for pruning, named revised subtree and revised local utility. To reduce dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. An array‐based utility‐counting technique is also utilised to calculate upper‐bound efficiently. EHIN utilises various properties and pruning strategies to mine HUIs with negative utility. The experimental results show that the proposed algorithm is 28 times faster, and it consumes up to 10 times less memory than the state‐of‐the‐art algorithm FHN. Moreover, a key advantage is that EHIN always performs better for dense datasets.

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Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

Singh

2019

International Journal of Data Warehousing and Mining

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High utility itemset (HUI) mining is one of the popular and important data mining tasks. Several studies have been carried out on this topic, which often discovers a very large number of itemsets and rules, which reduces not only the efficiency but also the effectiveness of HUI mining. In order to increase the efficiency and discover more interesting HUIs, constraint-based mining plays an important role. To address this issue, the authors propose an algorithm to discover HUIs with length constraints named EHIL (Efficient High utility Itemsets with Length constraints) to decrease the number of HUIs by removing tiny itemsets. EHIL adopts two new upper bound named sub-tree and local utility for pruning and modify them by incorporating length constraints. To reduce the dataset scans, the proposed algorithm uses transaction merging and dataset projection techniques. The execution time improvements ranged from a modest five percent to two orders of magnitude across benchmark datasets. The memory usage is up to twenty-eight times less than state-of-the-art algorithm FHM+.

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An Efficient Approach to Discovering Frequent Patterns from Data Cube using Aggregation and Directed Graph

Cited by 3 publications

References 6 publications

The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubes

The curse of indecomposable aggregates for big data exploratory analysis with a case for frequent pattern cubes

Mining of high‐utility itemsets with negative utility

Efficient Algorithm for Mining High Utility Pattern Considering Length Constraints

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