Mining High Utility Itemsets with Negative Utility Values in Transactional Databases

The goal of the high-utility itemset mining task is to discover combinations of items that yield high profits from transactional databases. HUIM is a useful tool for retail stores to analyze customer behaviors. However, in the real world, items are found with both positive and negative utility values. To address this issue, we propose an algorithm named Modified Efficient High‐utility Itemsets mining with Negative utility (MEHIN) to find all HUIs with negative utility. This algorithm is an improved version of the EHIN algorithm. MEHIN utilizes 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 utilized to calculate upper‐bound efficiently. The MEHIN employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on negative utility in retail databases in terms of runtime.

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Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2012.59 ◽

2013 ◽

Vol 25 (8) ◽

pp. 1772-1786 ◽

Cited By ~ 262

Author(s):

Vincent S. Tseng ◽

Bai-En Shie ◽

Cheng-Wei Wu ◽

Philip S. Yu

Keyword(s):

Efficient Algorithms ◽

Transactional Databases ◽

High Utility ◽

High Utility Itemsets

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Discovering Relative High Utility Itemsets in Very Large Transactional Databases Using Null-Invariant Measure

10.1109/bigdata52589.2021.9672064 ◽

2021 ◽

Author(s):

R. Uday Kiran ◽

Pradeep Pallikila ◽

J. M. Luna ◽

Philippe Fournier-Viger ◽

Masashi Toyoda ◽

...

Keyword(s):

Invariant Measure ◽

Transactional Databases ◽

High Utility ◽

High Utility Itemsets

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Mining High Utility Itemsets Based on Pattern Growth without Candidate Generation

Mathematics ◽

10.3390/math9010035 ◽

2020 ◽

Vol 9 (1) ◽

pp. 35

Author(s):

Yiwei Liu ◽

Le Wang ◽

Lin Feng ◽

Bo Jin

Keyword(s):

State Of The Art ◽

Research Topic ◽

Performance Gap ◽

Utility Values ◽

Original Dataset ◽

Pattern Growth ◽

Active Research ◽

High Utility ◽

High Utility Itemsets ◽

Mining Algorithms

Mining high utility itemsets (HUIs) has been an active research topic in data mining in recent years. Existing HUI mining algorithms typically take two steps: generating candidates and identifying utility values of these candidate itemsets. The performance of these algorithms depends on the efficiency of both steps, both of which are usually time-consuming. In this study, we propose an efficient pattern-growth based HUI mining algorithm, called tail-node tree-based high-utility itemset (TNT-HUI) mining. This algorithm avoids the time-consuming candidate generation step, as well as the need of scanning the original dataset multiple times for exact utility values, as supported by a novel tree structure, named the tail-node tree (TN-Tree). The performance of TNT-HUI was evaluated in comparison with state-of-the-art benchmark methods on different datasets. Experimental results showed that TNT-HUI outperformed benchmark algorithms in both execution time and memory use by orders of magnitude. The performance gap is larger for denser datasets and lower thresholds.

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Mining Top-k Regular High-Utility Itemsets in Transactional Databases

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2019010104 ◽

2019 ◽

Vol 15 (1) ◽

pp. 58-79 ◽

Cited By ~ 1

Author(s):

P. Lalitha Kumari ◽

S. G. Sanjeevi ◽

T.V. Madhusudhana Rao

Keyword(s):

High Efficiency ◽

Threshold Value ◽

Search Space ◽

List Structure ◽

High Profit ◽

Transactional Databases ◽

High Utility ◽

High Utility Itemsets ◽

Pruning Techniques ◽

Novel Algorithm

Mining high-utility itemsets is an important task in the area of data mining. It involves exponential mining space and returns a very large number of high-utility itemsets. In a real-time scenario, it is often sufficient to mine a small number of high-utility itemsets based on user-specified interestingness. Recently, the temporal regularity of an itemset is considered as an important interesting criterion for many applications. Methods for finding the regular high utility itemsets suffers from setting the threshold value. To address this problem, a novel algorithm called as TKRHU (Top k Regular High Utility Itemset) Miner is proposed to mine top-k high utility itemsets that appears regularly where k represents the desired number of regular high itemsets. A novel list structure RUL and efficient pruning techniques are developed to discover the top-k regular itemsets with high profit. Efficient pruning techniques are designed for reducing search space. Experimental results show that proposed algorithm using novel list structure achieves high efficiency in terms of runtime and space.

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