Incremental frequent itemsets mining based on frequent pattern tree and multi-scale

Xun, Yaling; Cui, Xiaohui; Zhang, Jifu; Yin, Qingxia

doi:10.1016/j.eswa.2020.113805

Cited by 23 publications

(15 citation statements)

References 17 publications

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“…For example, suppose α and β are 25% and 0.25, respectively in Table 1, minSup(D) is 1.25, which is the product of 5, the size of DB 0 , and 0.25, the α value. Since item i B exists in three transactions T 1 , T 3 , and T 5 , Sup B is ( ) 3. The utility occupancy uo B uo B T uo B T uo B T Sup B ( ) is calculated as{ ( , ) + ( , ) + ( , )}/ ( )…”

Section: Tidmentioning

confidence: 99%

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Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

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Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real‐time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state‐of‐the‐art algorithms.

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Section: Tidmentioning

confidence: 99%

“…Pattern mining, which is a branch of data mining, extracts the meaningful combinations of items called patterns from a given database. Frequent pattern mining [1][2][3] is a fundamental area of pattern mining. It finds patterns that occur more than a certain number of times in a given database.…”

Section: Introductionmentioning

confidence: 99%

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

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“…Xun et al 30 have established a combined frequent item set mining in conjunction with the multi-scale concept to define a new FP tree together with multiple scale incremental mining (FPMSIM) strategy for the datasets with assorted insights and hypothesis snags. The extensive experiments were done with increase in datasets shows better results in terms of accuracy.…”

Section: Related Workmentioning

confidence: 99%

A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

Devi¹

2021

Concurrency and Computation

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Data mining methods face various problems when extracting the data analysis process.These problems are solved based on the frequent pattern mining (FPM). In the data mining area, the frequent pattern (FP) extraction is a dominant embassy while processing the databases. Despite the fact that it has proven its importance in various mining provinces, the consistent research work of researchers has provided numerous efforts in this environment through substantial innovations. Based on the expected challenges of pattern mining, the time and memory consumption while mining the hidden FP increases over time by the conventional FPM approaches. This article proposes a Parallel Direct-Vertical Map Reduce Programming model to effectively extract the FPM in a dispersed environment and analyzed using the Sales transaction dataset as UCI Machine Learning Repository. The proposed model has two phases of working, namely, (i) mapping and (ii) reduction. Here, the proposed Parallel Direct-Vertical method is utilized while mapping. The parallelism of the proposed model is implemented and executed based on the number of clusters of databases in architecture-based software and its final outcome shows that the proposed model is more efficient than the existing methods.

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“…The most significant term involved during the mining of information is the knowledge discovery database (KDD). 3 Due to random changes in real-world social networking media, the data of social media changes swiftly containing large file sizes. 4 Nowadays, the concept of data mining is employed widely with the aim of knowledge discovery and data explosion from huge datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The main intention of pattern mining is involved in discovering various hidden patterns, mining knowledge and extracting valuable information. The most significant term involved during the mining of information is the knowledge discovery database (KDD) 3 . Due to random changes in real‐world social networking media, the data of social media changes swiftly containing large file sizes 4 .…”

Section: Introductionmentioning

confidence: 99%

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

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Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre-processing phase, data pre-processing, frequent itemset mining, and ARM. In data pre-processing phase, the collected twitter datasets are pre-processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid-19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

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Incremental frequent itemsets mining based on frequent pattern tree and multi-scale

Cited by 23 publications

References 17 publications

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

A Parallel Direct‐Vertical Map Reduce Programming model for an effective frequent pattern mining in a dispersed environment

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

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