Determination of the relationship pattern of association topic on Al-Qur’an using FP-Growth Algorithms

Novita, Rice; Mustakim, Mustakim; Salisah, Febi Nur

doi:10.1088/1757-899x/1088/1/012020

Cited by 7 publications

(7 citation statements)

References 7 publications

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“…Data mining is a component of the Knowledge Discovery in Databases (KDD) process, which also includes the discovery of an algorithm that classifies data patterns based on their applicability to data analysis [32]. Data mining involves the extraction of valuable insights and patterns from large datasets, enabling organizations to make informed decisions and predictions.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Al-Badani,

Al-Khulaidi

2023

jast

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Frequent set-item mining is a widely used technique in the field of data mining. The algorithm utilized in Association Rules constitutes the primary source of contention; this approach is memory-intensive and time-intensive. The extraction process for concealed patterns of frequent item sets, on the other hand, becomes more time-consuming as the volume of data grows. Therefore, the necessary algorithm for mining the patterns of frequently occurring concealed item sets must be memory-efficient and fast-running. By analyzing various algorithms for locating frequent item sets, this paper aims to contribute to the development of a more efficient algorithm in this domain. The paper will evaluate the efficiency of different algorithms by considering their memory usage and runtime. Additionally, it will explore potential improvements to existing algorithms to enhance their performance in mining frequently occurring concealed item sets.

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Section: Data Mining Techniquesmentioning

confidence: 99%

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Al-Badani,

Al-Khulaidi

2023

jast

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“…Frequent Pattern Growth (FP-Growth) is a data mining method introduced by Han et al (2000) to extract association rules between products (items) in a transactional dataset (Han et al, 2000). FP-Growth uses the Frequent Pattern Tree (FP-tree) to generate the frequency of occurrence (frequent itemset) of each product (Novita, Mustakim, & Salisah, 2021).…”

Section: Frequent Pattern Growthmentioning

confidence: 99%

“…Meanwhile, to find the support value of the 2-itemset, for example, items A and B, it can be calculated using (2) (Novita et al, 2021).…”

Section: Frequent Pattern Growthmentioning

confidence: 99%

Marketing Strategy Using Frequent Pattern Growth

Suhandi

Gustriansyah

2021

CNAHPC

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The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.

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“…The application of data mining can be made with the FP-Growth Algorithm. Frequent Pattern Growth (FP-Growth) is an alternative algorithm that can be used to determine the most frequently occurring data set (frequent itemset) in a data set [2]. Previous studies used FP-Growth to find patterns in items purchased together [1].…”

Section: Introductionmentioning

confidence: 99%

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

Triana

Khoerida

Widiawati

et al. 2022

IOTA

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Along with the rapid development of the culinary industry, business competition is also getting tougher. Warung Oemah Tani serves a variety of menus and drinks, but to provide satisfying service to customers, business people must try to develop new products. Under these circumstances, the menu recommendations for Warung Oemah Tani need to be analyzed so that the recommendations made are right on target. This study aims to analyze the sales of Warung Oemah Tani using the FP Growth algorithm. This algorithm identifies the data set with the highest frequency of concurrent sales (frequent itemset). The results of the association rules show that the highest support value is 0.520 and the highest confidence value is 0.929, with a minimum support of 30% and a minimum confidence of 80%. Obtained 14 rule associations that meet the minimum support and minimum confidence.

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Determination of the relationship pattern of association topic on Al-Qur’an using FP-Growth Algorithms

Cited by 7 publications

References 7 publications

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Analysis Of Mining Algorithms for Patterns of Frequent Item sets and Hidden

Marketing Strategy Using Frequent Pattern Growth

Implementation of the FP-Growth Algorithm in Sales Transactions for Menu Package Recommendations at Warung Oemah Tani

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