An Adaptive Method for Mining Frequent Itemsets Based on Apriori And FP Growth Algorithm

Hasan, Md. Mahamud; Mishu, Sadia Zaman

doi:10.1109/ic4me2.2018.8465499

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…This system is able to provide an efficient and custom services, enabling readers to find the books of their interest more quickly. In the journal, Hasan et al [32] suggested a technique for avoiding this issue by using binomial distribution (BD) to adapt to discover adequate minimum assistance. Mine optimal frequency itemsets have been helped to make their suggested method work better than the current benchmark.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Sornalakshmi

Balamurali

Venkatesulu

et al. 2020

Neural Comput & Applic

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Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient's medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA-SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises ''similar'' concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Sornalakshmi

Balamurali

Venkatesulu

et al. 2020

Neural Comput & Applic

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“…The frequent itemset mining algorithm (Apriori algorithm) [ 33 , 34 , 35 , 36 ] used for I = {

,…,

} is the set of all items in the data, while T = {

,…,

} is the set of all transactions. A collection of 0 or more items is called an itemset.…”

Section: Detailed Description Of the Schemementioning

confidence: 99%

Trusted Data Storage Architecture for National Infrastructure

Wang

Rui

Xiao-long

et al. 2022

Sensors

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National infrastructure is a material engineering facility that provides public services for social production and residents’ lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.

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“…However, the sizes of three data structures are set empirically. The work in Reference 31 finds the most suitable minimum support adaptively by Binomial distribution. It is able to optimize the running time on some specific datasets, but fails to do so if the minimum support changes.…”

Section: Related Workmentioning

confidence: 99%

On a parallel spark workflow for frequent itemset mining based on array prefix‐tree

Niu

et al. 2021

Concurrency and Computation

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Extracting frequent itemsets from datasets is an important problem in data mining, for which several mining methods including FP-Growth have been proposed. FP-Growth is a classical frequent itemset mining method, which generates pattern databases without candidates. Many improvements have been made in the literature due to the high time complexity and memory usage of FP-Growth. However, most of them still suffer from performance issues on large datasets. In this paper, we design an auxiliary structure, Array Prefix-Tree (AP-Tree), and propose a new algorithm, Array Prefix-Tree Growth (APT-Growth), which is further parallelized as a Spark workflow, referred to as PAPT-Growth. Based on a density threshold, we incorporate an adaptive algorithm selection process into PAPT-Growth to ensure its running time performance. We conduct extensive experiments on different thresholds and multiple datasets, and experimental results show the performance superiority of PAPT-Growth in comparison with several state-of-the-art methods such as PFP, YAFIM, and DFPS. The analysis on density reveals a changing point, which justifies the necessity and validity of adaptive algorithm selection.

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An Adaptive Method for Mining Frequent Itemsets Based on Apriori And FP Growth Algorithm

Cited by 12 publications

References 8 publications

RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Trusted Data Storage Architecture for National Infrastructure

On a parallel spark workflow for frequent itemset mining based on array prefix‐tree

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