A weighted N-list-based method for mining frequent weighted itemsets

Bui, Huong Mai; Vo, Bay; Nguyen, Ham; Nguyen-Hoang, Tu-Anh; Hong, Tzung-Pei

doi:10.1016/j.eswa.2017.10.039

Cited by 39 publications

(14 citation statements)

References 22 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%

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%

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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“…Vo et al also used the N-list structure in searching for closed patterns and FPs [23,24]. In 2017, these authors proposed using the N-list structure in the INLA-MFP algorithm [24] to mine maximal patterns, and after that they went on with the use of the N-list structure in the NFWI [12] algorithm to obtain frequent weighted patterns. The negFIN [20] based on the structure similar to the N-list structure, was also proposed to quickly mine FPs.…”

Section: B N-list Structurementioning

confidence: 99%

“…In recent years, many methods have also been proposed to mine various types of frequent patterns, such as NFWI [12] for frequent weighted itemset mining, HMiner-Closed [13], MEFIM and iMEFIM [14], dHAUIM [15], HAUP-growth [16] for mining high-utility patterns, and MPFPSBFS and MPFPSDFS [17] for mining periodic patterns in multiple sequences. Deng et al proposed the N-List structure and PrePost algorithm [18] to mine frequent patterns efficiently.…”

Section: Introductionmentioning

confidence: 99%

An N-List-Based Approach for Mining Frequent Inter-Transaction Patterns

Nguyen

et al. 2020

IEEE Access

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Mining frequent inter-transaction patterns (ITPs) from large databases is both useful and of interest. Since frequent inter-transaction patterns (FITPs) are discovered across transactions in a transaction database (TD), the number of patterns is very large. Therefore, the mining time and memory usage are very high. Although several algorithms have been proposed for mining FITPs, they still require long runtime and high memory usage. Recent research shows that N-list-based approaches are very efficient for mining frequent patterns (FPs). Therefore, in this paper, we propose an N-list-based algorithm, called NL-ITP-Miner, to mine FITPs. In the proposed algorithm, we adopt the advantages of the N-list structure to build up the IT-PPC-tree. During the process of building the IT-PPC-tree, NL-ITP-Miner applies our proposed theorems to eliminate infrequent inter-transaction 1-patterns to reduce the search space. NL-ITP-Miner scans the database once to find frequent inter-transaction (FIT) 1-patterns for constructing the IT-PPC-tree, after that, the NL-ITP-Miner algorithm traverses this tree to generate frequent 1-patterns, FIT 1-patterns with their respective N-lists. Besides, we also propose effective pruning strategies that help NL-ITP-Miner to reduce the search space significantly and generate FITPs more quickly. Experiments show that NL-ITP-Miner outperforms the state-of-the-art algorithms for mining FITPs in terms of runtime and memory usage. INDEX TERMS Data mining; inter-transaction patterns; internet of things; pattern mining.

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“…As the types of data processed by the system are diversified, applied studies to analyze various types of data have been continuously proposed. For example, examples of applied pattern mining are weighted pattern mining, 11,12 high utility pattern mining, 13,14 top-k pattern mining, [15][16][17] high average utility pattern mining, 18 sequential pattern mining, [19][20][21] and maximal pattern mining. [22][23][24] Applied pattern mining approaches are utilized in the systems involving real life, such as mining clickstream patterns to show interactions between users and websites, 25 mining noisy databases, 19 and recognizing air quality data.…”

Section: Introductionmentioning

confidence: 99%

Efficiently mining erasable stream patterns for intelligent systems over uncertain data

et al. 2020

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Data mining is a method for extracting useful information that is necessary for a system from a database. As the types of data processed by the system are diversified, the transformed pattern mining techniques for processing these type of data have been proposed. Unlike the traditional pattern mining methods, erasable pattern mining is a technique for finding the patterns that can be removed by coming with a small profit. Erasable pattern mining should be able to process data by considering both the environment that the data are generated from and the characteristics of the data. An uncertain database is a database that is composed of uncertain data. Since erasable patterns discovered from uncertain data contain significant information, these patterns need to be extracted. In addition, databases gradually increase, because the data from various fields is generated and accumulated over data streams. Data streams should be processed as intelligently as possible to provide the useful data to the system in real time. In this paper, we propose an efficient erasable pattern mining algorithm that processes uncertain data that is generated over data streams. The uncertain erasable patterns discovered through the suggested technique are more meaningful information by considering the probability of the item and the profit. Moreover, the proposed method can perform efficient mining operations by using both tree and list structures. The performance of the suggested algorithm is verified through the performance tests compared with state‐of‐the‐art algorithms using real data sets and synthetic data sets.

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A weighted N-list-based method for mining frequent weighted itemsets

Cited by 39 publications

References 22 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

An N-List-Based Approach for Mining Frequent Inter-Transaction Patterns

Efficiently mining erasable stream patterns for intelligent systems over uncertain data

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