A Guided FP-growth algorithm for multitude-targeted mining of big data

Shabtay, Lior; Yaari, Rami; Dattner, Itai

doi:10.48550/arxiv.1803.06632

Cited by 3 publications

(5 citation statements)

References 31 publications

(71 reference statements)

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“…Their designed structures can be updated incrementally with new transactions. Shabty et al [14] designed a novel method called Guided FP-growth (GFP-growth) for multitude-targeted mining. The fast and generic tool GFP-growth can determine the frequency of a given large list of itemsets, which serve as the targets, in a large dataset from an FP-tree [45] based on Target Itemset Tree.…”

Section: Target-oriented Queryingmentioning

confidence: 99%

“…To satisfy the requirements of users, a series of frequency-based methods that can search for specific goal items were proposed. Till now, target-oriented frequent itemset querying [14], association rule querying [15], [16], [17], and sequential pattern querying (SPQ) [18], [19], [20] have performed significant roles in querying in the database. The three target-oriented technologies can efficiently excavate patterns and rules involving a subset of certain items, such as targeted queries, and have shown significant potential in several real-life situations [21].…”

Section: Introductionmentioning

confidence: 99%

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TUSQ: Targeted High-Utility Sequence Querying

Zhang¹,

Du²,

Dai³

et al. 2021

Preprint

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Significant efforts have been expended in the research and development of a database management system (DBMS) that has a wide range of applications for managing an enormous collection of multisource, heterogeneous, complex, or growing data. Besides the primary function (i.e., create, delete, and update), a practical and impeccable DBMS can interact with users through information selection, that is, querying with their targets. Previous querying algorithms, such as frequent itemset querying and sequential pattern querying (SPQ) have focused on the measurement of frequency, which does not involve the concept of utility, which is helpful for users to discover more informative patterns. To apply the querying technology for wider applications, we incorporate utility into target-oriented SPQ and formulate the task of targeted utility-oriented sequence querying. To address the proposed problem, we develop a novel algorithm, namely targeted high-utility sequence querying (TUSQ), based on two novel upper bounds suffix remain utility and terminated descendants utility as well as a vertical Last Instance Table structure. For further efficiency, TUSQ relies on a projection technology utilizing a compact data structure called the targeted chain. An extensive experimental study conducted on several real and synthetic datasets shows that the proposed algorithm outperformed the designed baseline algorithm in terms of runtime, memory consumption, and candidate filtering.

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Section: Target-oriented Queryingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TUSQ: Targeted High-Utility Sequence Querying

Zhang¹,

Du²,

Dai³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The experiments demonstrated that the new data structure is up to 43% smaller than IT on average in terms of memory consumption. As an FP-tree-based algorithm, guided FP-growth (GFP-growth) [18] was proposed. It can determine the support of a given large list of itemsets (that are regarded as targets) using the target itemset tree.…”

Section: Target Pattern Queryingmentioning

confidence: 99%

“…Researchers have defined this interesting task as target-oriented pattern mining [17]. To date, several studies have been conducted on target-based pattern mining, such as target-oriented frequent itemset querying [18], target-based association rule mining [19], [20], and targeted sequential pattern querying [21]- [23]. As stated previously, customers are usually not interested in receiving all discount messages according to the recommender system.…”

Section: Introductionmentioning

confidence: 99%

TargetUM: Targeted High-Utility Itemset Querying

Miao¹,

Wan²,

Gan³

et al. 2021

Preprint

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Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (minUtil) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.

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“…Therefore, mining user-specified itemsets was proposed as a new interesting task, named targeted utility mining (abbreviated as TaUM) [29], [30]. To our best knowledge, there are some studies about targetbased pattern mining such as target-oriented frequent itemset querying [31], target-based association rule mining [32], [33] and sequential pattern querying [34]- [36]. However, there is little information in the literature about TaUM.…”

Section: Introductionmentioning

confidence: 99%

Towards Target High-Utility Itemsets

Miao¹,

Gan²,

Wan³

et al. 2022

Preprint

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For applied intelligence, utility-driven pattern discovery algorithms can identify insightful and useful patterns in databases. However, in these techniques for pattern discovery, the number of patterns can be huge, and the user is often only interested in a few of those patterns. Hence, targeted mining of high-utility itemsets has emerged as a key research topic, where the aim is to find a subset of patterns that meet a target pattern constraint instead of all patterns. This is a challenging task because efficiently finding tailored patterns in a very large search space requires a targeted mining algorithm. A first algorithm called TargetUM has been proposed, which adopts an approach similar to post-processing using a tree structure, but the running time and memory consumption are unsatisfactory in many situations. In this paper, we address this issue by proposing a novel list-based algorithm with pattern matching mechanism, named THUIM (Targeted High-Utility Itemset Mining), which can quickly match high-utility itemsets during the mining process to select the target patterns. Extensive experiments were conducted on different datasets to compare the performance of the proposed algorithm with state-of-theart algorithms. Results show that THUIM performs very well in terms of runtime and memory consumption, and has good scalability compared to TargetUM.

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A Guided FP-growth algorithm for multitude-targeted mining of big data

Cited by 3 publications

References 31 publications

TUSQ: Targeted High-Utility Sequence Querying

TUSQ: Targeted High-Utility Sequence Querying

TargetUM: Targeted High-Utility Itemset Querying

Towards Target High-Utility Itemsets

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