BAHUI: Fast and Memory Efficient Mining of High Utility Itemsets Based on Bitmap

@article{Song2014BAHUIFA,
  title={BAHUI: Fast and Memory Efficient Mining of High Utility Itemsets Based on Bitmap},
  author={Wei Song and Yu Liu and Jinhong Li},
  journal={Int. J. Data Warehous. Min.},
  year={2014},
  volume={10},
  pages={1-15}
}
Mining high utility itemsets is one of the most important research issues in data mining owing to its ability to consider nonbinary frequency values of items in transactions and different profit values for each item. Although a number of relevant approaches have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. In this paper, the authors propose an efficient algorithm, namely BAHUI Bitmap-based Algorithm for High… 
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TLDR
An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\(^2\)HUP, HUI-Miner, HUP-M Miner, FHM and UP-Growth+.
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Two new stricter upper bounds are designed to reduce the computation time by refraining from visiting unnecessary nodes of an itemset, so that the search space of the potential HUIs can be greatly reduced, and the mining procedure of the execution time can be improved.
FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining
TLDR
An efficient algorithm called FCHUIM is used to mine a concise representation called the frequent closed high-utility itemset (FCHUI), which is based on a total summary list structure for storing and retrieving utility lists without repeatedly scanning the database.
FHM + : Faster High-Utility Itemset Mining Using Length Upper-Bound Reduction
TLDR
To discover HUIs efficiently with length constraints, FHM\(+\) introduces the concept of Length Upper-Bound Reduction (LUR), and two novel upper-bounds on the utility of itemsets, and shows that length constraints are effective at reducing the number of patterns.
CLS-Miner: efficient and effective closed high-utility itemset mining
TLDR
This paper proposes a novel algorithm called CLS-Miner, which utilizes the utility-list structure to directly compute the utilities of itemsets without producing candidates, and introduces three novel strategies to reduce the search space, namely chain-estimated utility co-occurrence pruned, lower branch pruning, and pruning by coverage.
Optimized High-Utility Itemsets Mining for Effective Association Mining Paper
TLDR
Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.
An efficient algorithm for mining top-k on-shelf high utility itemsets
TLDR
An efficient algorithm named KOSHU (fast top-K on- shelf high utility itemset miner) is proposed to mine the top-k HOUs efficiently, while considering on-shelf time periods of items, and items having positive and/or negative unit profits.
A New Algorithm for High Average-utility Itemset Mining
TLDR
A new algorithm is proposed which uses a new list structure and pruning strategy which outperforms the state-of-the-art HAUIM algorithms in terms of runtime and memory consumption.
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