Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation

Qu, Junfeng; Liu, Mengchi; Fournier‐Viger, Philippe

doi:10.1007/978-3-030-04921-8_5

Cited by 24 publications

(18 citation statements)

References 29 publications

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“…This is a very powerful property to reduce the search space, but it does not hold for the utility measure in HUIM. To mine high utility itemsets efficiently, state-of-the-art HUIM algorithms such as Two-Phase [14], HUI-Miner [15], d2hup [13] and HU-FIMi [10] introduced various upper-bounds on the utility measure that respect the anti-monotonicity property to reduce the search space, and novel data structures to perform utility computation efficiently.…”

Section: Related Workmentioning

confidence: 99%

“…Another property of TU-lists is that those of two itemsets of the form P ∪{x} and P ∪ {y} can be joined to obtain the TU-list of an itemset P ∪ {x, y}. This is done by first applying the construct procedure of HUI-Miner [15]. Then, the binU tils, binRutils, trendP eriods and promisingP eriods lists can be calculated by applying the findTrend procedure, presented in the next section.…”

Section: Property 1 (Pruning a Low-twu Item In A Database)mentioning

confidence: 99%

“…Frequent itemset mining (FIM) is a popular data mining task, which consists of enumerating all sets of values (items) that have a support (occurrence frequency) that is no less than a minimum threshold in a transaction database [5]. FIM has recently been generalized as high utility itemset mining (HUIM) to consider items having non binary purchase quantities in transactions and weights indicating their relative importance [2,4,10,[13][14][15]. The goal of HUIM is to find all itemsets that have a high utility (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Mining Locally Trending High Utility Itemsets

Fournier-Viger

Yang

Lin

et al. 2020

Advances in Knowledge Discovery and Data Mining

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High utility itemset mining consists of identifying all the sets of items that appear together and yield a high profit in a customer transaction database. Recently, this problem was extended to discover trending high utility itemsets (itemsets that yield an increasing or decreasing profit over time). However, an important limitation of that problem is that it is assumed that trends remain stable over time. But in real-life, trends may change in different time intervals due to specific events. To identify time intervals where itemsets have increasing/decreasing trends in terms of utility, this paper proposes the problem of mining Locally Trending High Utility Itemsets (LTHUIs) and their Trending High Utility Periods (THUPs). Properties of the problem are studied and an efficient algorithm named LTHUI-Miner is proposed to enumerate all the LTHUIs and their THUPs. An experimental evaluation shows that the algorithm is efficient and can discover insightful patterns not found by previous algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Property 1 (Pruning a Low-twu Item In A Database)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining Locally Trending High Utility Itemsets

Fournier-Viger

Yang

Lin

et al. 2020

Advances in Knowledge Discovery and Data Mining

Self Cite

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“…Many novel concepts of upper bounds were introduced in one-phase algorithms such as remaining utility, local-utility, sub-tree utility etc. HUI-Miner was the first design in one-phase algorithms (Liu & Qu, 2012;Fournier et al, 2018). Many optimal versions have also been designed for this algorithm such as HUP-Miner, HUI-Miner*, mHUIMiner etc.…”

Section: B Group B-one Phase Algorithmmentioning

confidence: 99%

Various Research Opportunities in High Utility Itemset Mining

Dalal*,

Dahiya

2019

IJRTE

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Pattern mining is a technique, which discovers interesting, hidden, unpredicted and useful patterns of data from the database. Most of the research work in pattern mining has been focused on the traditional way of Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) for patterndiscovery. Patterns in frequent itemset mining are based on the occurrence frequency of items. Although frequent pattern mining is useful, the assumption that ‘frequent patterns are interesting,’ doesn’t hold for numerous applications. High Utility Itemset Mining (UIM) overcomes this limitation of frequent itemset mining. The aim of HUIM is to find the patterns based on a utility function where the utility can be measured in terms of revenue, profit, weight, frequency, interestingness or time spent on some webpage, etc. Mining patterns with high utility can be seen as a generalization of FIM where the transaction database is the input and every item is having a utility factor representing its importance and might have non-binary quantities in the transactions. This paper surveys various recent advances and research opportunities in the field of high utility itemset mining

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“…A high utility miner without candidate generation is proposed to organize the utility lists in a quick manner. The lists of utilities are arranged in a horizontal manner to improve pruning mechanism (13). The frequent and high utility itemsets are identified using metaheuristic approaches including sward intelligence, ant colony optimization and genetic algorithms.…”

Section: Related Artmentioning

confidence: 99%

Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

Pazhaniraja¹,

Sountharrajan²,

Suganya³

et al. 2018

EAI Endorsed Transactions on Energy Web

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High-utility based itemset mining is the advancement of recurrent pattern mining that discovers occurrence of frequent transactions from a huge database. The issues in frequent pattern mining involve the elimination of quantities purchased by the customers and cost of purchased product. This can be resolved by high utility itemset mining which includes quantities and profit of the products in the transactions. The conventional association rule mining algorithms results in huge memory consumption due to the complexity in pruning the search space. In this paper, machine learning based high-utility itemset mining is applied to predict next order in an online grocery store depending on the transactions. The overall goal is to enhance the business profitability by stocking the high utility items in market. The Top 'N' variant Random Forest model is proposed to recommend the high utility itemsets, thereby predicting the reordered/next ordered items. The model is evaluated using Instacart market dataset to measure accuracy, precision and recall.

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Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation

Cited by 24 publications

References 29 publications

Mining Locally Trending High Utility Itemsets

Mining Locally Trending High Utility Itemsets

Various Research Opportunities in High Utility Itemset Mining

Top ‘N’ Variant Random Forest Model for High Utility Itemsets Recommendation

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