A hybrid framework for mining high-utility itemsets in a sparse transaction database

Dawar, Siddharth; Goyal, Vikram; Bera, Debajyoti

doi:10.1007/s10489-017-0932-1

Cited by 38 publications

(20 citation statements)

References 30 publications

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“…Following that, many techniques were discovered to effectively exploit HUIs without candidate generation, including d2HUP [30] (proposed by Liu et al in 2016) and EFIM [17] (proposed by Zida et al in 2017). By integrating the two algorithms UP-Growth+ and FHM, Dawar et al [20] proposed a hybrid algorithm UFH to effectively exploit HUIs on sparse databases in 2016. In 2018, Duong et al [31] built a new utility-list buffer (ULB) structure that evolved from the previous utility-list structure and proposed the ULB-Miner (Utility-List Buffer for high utility itemset Miner) algorithm using an effective method for creating utility-list segments to reduce the cost of constructing utility-lists, so HUIs are mined more efficiently both in terms of runtime and memory consumption.…”

Section: A High-utility Itemset Miningmentioning

confidence: 99%

“…In particular, the algorithm excels when the database contains many long transactions or low minimum utility thresholds. In 2016, Dawar et al [20] presented the UFH algorithm to exploit HUIs efficiently on the sparse dataset. Moreover, on the sparse dataset, some other techniques also presented to discovery useful knowledge such as randomized latent factor model [21], distributed alternative stochastic gradient descent model DASGD [22] or model based on SGD extensions [23].…”

Section: Introductionmentioning

confidence: 99%

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Mining Correlated High Utility Itemsets in One Phase

et al. 2020

IEEE Access

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High-utility itemset mining (HUIM) in transaction databases has been extensively studied to discover interesting itemsets from users' purchase behaviors. With this, business managers can adjust their sale strategies appropriately to increase profit. HUIM approaches usually focus on the utility values of itemsets, but rarely evaluate the correlation of items in itemsets. Many high-utility itemsets are weakly correlated and have no real meaning. To address this issue, we suggest an algorithm, called CoHUI-Miner, to efficiently find correlated high-utility itemsets. In the proposed algorithm, we use the database projection mechanism to reduce the database size and present a new concept, called the prefix utility of projected transactions, to eliminate itemsets which do not satisfy the minimum threshold in the mining process. Experimental evaluation on two types of datasets from sparse to dense ones shows that CoHUI-Miner can efficiently mine correlated high-utility itemsets with regard to both execution time and memory usage. INDEX TERMS Correlated high-utility itemset, data mining, high-utility pattern, projected database.

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Section: A High-utility Itemset Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Correlated High Utility Itemsets in One Phase

et al. 2020

IEEE Access

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“…Recently, Dawar et al [25] proposed a hybrid framework for mining HUIs. This algorithm is more effective than the FHM [18] and EFIM algorithms [10], especially on dense datasets.…”

Section: High-utility Itemset Miningmentioning

confidence: 99%

“…Itemset is used; if R has a utility confidence value (R.uconf ) greater than or equal to min-uconf (line 24), R is added into the results (line [25][26][27]. If R is valid, Q is enqueued in the list of child nodes of L i (lines 32-37).…”

Section: Algorithmmentioning

confidence: 99%

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

Mai

Nguyen

et al. 2020

Sensors

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In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.

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“…Therefore, HUI is determined as an itemset whose utility is not less than the minutil set by the user. HUIM [16][17][18][19][20][21][22][23][24][25] is an interesting research area, where researchers can focus on the values of items or the profits of commodities. HUIM is a difficult task because the utility measure used in HUIM is different from the support measure used in FIM; it is not antimonotonic.…”

Section: Introductionmentioning

confidence: 99%

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

et al. 2020

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Mining a closed high-utility itemset is a prevalent research task in analyzing transaction databases. However, numerous target itemsets are generated in the closed high-utility itemset mining task. As a result, too many closed high-utility itemsets (CHUIs) will not only increase the time and memory consumption but also make it difficult for users to analyze the results. To address this problem, this paper proposes an efficient algorithm called FCHUIM, which is used to mine a concise representation called the frequent closed high-utility itemset (FCHUI). FCHUIM is based on a total summary list structure for storing and retrieving utility lists without repeatedly scanning the database. In addition, the proposed algorithm uses an effective upper bound on the utility of items to efficiently reduce the search space. Furthermore, a precheck method and a nested list structure are also introduced in this work to quickly discover FCHUIs. To test the performance of FCHUIM, we conduct extensive experiments on real-life and synthetic databases. We also select two state-of-the-art algorithms to compare with FCHUIM: CHUI-Miner and CLS-Miner. The results show that FCHUIM performs well on databases, whether the databases are dense or sparse.

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A hybrid framework for mining high-utility itemsets in a sparse transaction database

Cited by 38 publications

References 30 publications

Mining Correlated High Utility Itemsets in One Phase

Mining Correlated High Utility Itemsets in One Phase

Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

FCHUIM: Efficient Frequent and Closed High-Utility Itemsets Mining

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