Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy

Bui, Huong Mai; Vo, Bay; Nguyen-Hoang, Tu-Anh; Yun, Unil

doi:10.1007/s10489-020-01899-7

Cited by 16 publications

(10 citation statements)

References 44 publications

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“…Therefore, Lee et al [6] used two new prefix tree structures FWI-tree W and FWI-tree T , to propose two algorithms FWI * WSD and FWI * TCD, respectively, for mining FWPs effectively. Later, using the N-list-based structure, Bui et al [7] proposed the algorithm NFWI for mining FWPs, Le et al [8] presented TFWIN + for mining toprank-k FWPs, and Bui et al [9] developed NFWCI for mining frequent weighted closed patterns (FWCPs).…”

Section: A Mining Frequent Weighted Patternsmentioning

confidence: 99%

“…The results of experiments in this study confirmed that NFWI performs better than the existing approaches for mining FWPs. Later, using the WN-list structure combined with an early pruning strategy, Le et al [8] and Bui et al [9] proposed TFWIN + and NFWCI for mining top-rank-k FWPs and FWCPs, respectively.…”

Section: N-list-based Structuresmentioning

confidence: 99%

“…Mining frequent patterns (FPs) [1], [2] is a topic in artificial intelligence that has attracted much research interest in recent times. Currently, many variations of FPs such as frequent weighted patterns (FWPs) [3]- [9], erasable pattern mining [10]- [13], high utility pattern mining [14]- [17], and high average utility pattern mining [18], [19] have been developed, with many different usage scenarios. In several situations, items in a transaction database can have different importance levels.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in retail applications, products with high prices can contribute more to total revenue even though they appear in only a few transactions. In these scenarios, the concept of FWPs [3]- [9] is more suitable for practice than traditional FPs, because it considers the different weights of items. Therefore, it plays a crucial role in such scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

Bui¹,

Nguyen-Hoang²,

et al. 2021

IEEE Access

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The mining of frequent weighted patterns (FWPs) that considers the different semantic significance (weight) of items is more suitable for practice than the mining of frequent patterns. Therefore, it plays a vital role in real-world scenarios. However, there exist several limitations when applying methods for mining FWPs designed for static data on growth datasets, especially data streams. Hence, this study proposes an algorithm for mining FWPs over data streams. First, we introduce the concept of mining FWPs over data streams via a sliding window model. Then, we introduce a modification of the weighted node tree (WN-tree) named SWN-tree that has the ability to maintain the information over data streams. Next, this study develops a method for mining FWPs over data streams employing a sliding window model based on SWN-tree. This method is called FWPODS (Frequent Weighted Patterns Over Data Stream) algorithm. Finally, we conduct empirical experiments to compare the performances of our approach and the state-of-the-art algorithm (NFWI) for mining FWPs over data streams. The results of experiment indicate that our approach outperforms the NFWI algorithm when running in batch mode in a sliding window.INDEX TERMS pattern mining, data streams, frequent weighted patterns, sliding window model.

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Section: A Mining Frequent Weighted Patternsmentioning

confidence: 99%

Section: N-list-based Structuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

Bui¹,

Nguyen-Hoang²,

et al. 2021

IEEE Access

Self Cite

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show abstract

“…Table III, presents the timeline of some frequent itemsets mining algorithm on the transaction database with weighted items from 1998 to present, including information fields: top author's name, algorithm's name, satisfy Apriori property, with the data structure approach, the number of citations of the work (Cai [11] to Bui [50]), the year of publication and the group of algorithms.…”

Section: Some Algoritms For Mining the Frequent Itemsets With Weighted Itemsmentioning

confidence: 99%

A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items

Phan¹,

Lê²

2021

MIC ICT Research Journal

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In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binarytransactional database with unweighted items - This algorithmis essential in finding hindden relationships among items inyour data. Until 1998, with the development of various typesof transactional database - some researchers have proposed afrequent itemsets mining algorithms on transactional databasewith weighted items (the importance/meaning/value of itemsis different) - It provides more pieces of knowledge thantraditional frequent itemsets mining. In this article, the authors present a survey of frequent itemsets mining algorithmson transactional database with weighted items over the pasttwenty years. This research helps researchers to choose theright technical solution when it comes to scale up in big datamining. Finally, the authors give their recommendations anddirections for their future research.

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Mining high utility pattern with negative items in dynamic databases

Han

Zhang

Wang

et al. 2021

Int J of Intelligent Sys

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High utility pattern mining with negative items (HUPMN) has more practical applications because it can process data with negative utility values. But the existing HUPMN algorithms assume that the database is static and it would be very expensive to use these algorithms directly to deal with dynamic databases. To cope with this challenge, an high utility pattern algorithm for mining negative items from an incremental database is proposed for the first time, and an incremental index list structure is designed, which uses index values to quickly access and update the information stored in the list. In addition, A memory reuse strategy is also applied to reduce memory usage. Finally, a HUPMN algorithm based on sliding window was proposed, which can quickly update item information when the window was sliding. Extensive experimental evaluations have been carried out on a variety of data sets, and the results show that the proposed algorithm exhibits excellent performance in terms of running time and memory usage.

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Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy

Cited by 16 publications

References 44 publications

A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

A Sliding Window-Based Approach for Mining Frequent Weighted Patterns Over Data Streams

A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items

Mining high utility pattern with negative items in dynamic databases

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