FP-Growth Algorithm Research Articles

Comparison of Apriori and FP-Growth Algorithms in Analyzing Association Rules

The problem objectives of this research include the following: To implement Apriori and FP-Growth Algorithms in determining the comparison of association rules and To build a jupyter notebook application model in determining the comparison of association rules of Apriori and FP-Growth Algorithms. This research compares Apriori and FP-Growth algorithms in analyzing association rules, with a focus on implementation and model development in Jupyter Notebook. Through manual calculation using 10 transaction data samples and testing on 38,765 groceries data entries from Kaggle, differences were found in the lift results between itemsets. Apriori algorithm often shows a negative relationship between items, while FP-Growth gives a similar interpretation but with slightly different lift values, showing a different influence in the relationship between items. In addition, FP-Growth proved to be more efficient with a much faster execution time (5.2757 seconds) than Apriori (185.9585 seconds), especially in handling large datasets. The results of this study indicate that the selection of an appropriate algorithm should consider the characteristics of the dataset and the purpose of the analysis.

Menyusun Display Barang dengan Data Mining Assosiation Rule Menggunakan Rapid Miner Algoritma FP-GROWTH

A cosmetic cheerful shop located in the Marelan area of Medan city is a shop that sells women's cosmetic equipment. The owner of a cheerful cosmetics shop is very disciplined in recording sales and purchases of goods. Every transaction is always recorded and stored in the Microsoft Excel application. Meanwhile, the Ceria Cosmetics store has been running since 2015 and has recorded lots of transactions, both sales and purchases. From 2015 until now the data has been increasing, and so far this data has only been a record and stored in a folder on the store's computer. From the piles of transaction data at the cheerful cosmetics store, the authors took the idea of research to utilize this pile of data. One of the applications of information technology that can be used to utilize the pile of data is the data mining technique. By using data mining, a pile of data from a cheerful cosmetic store can be used to find association patterns. And the application of data mining with association rules can use the FP-Growth algorithm. The FP-Growth algorithm can be used as an alternative to determine the most frequently occurring data set (frequent itemset) in a data set. Later, the combination patterns obtained in this study can be used to arrange the layout of goods (cosmetics) at the Ceria Cosmetics store in Medan Marelan.

Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space

There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.

Data Management Analysis for Predicting Stroke using RapidMiner

Stroke are known as the second most leading cause of death. Because of this, data mining techniques are already being used to predict patients that may have stroke. Therefore, we are doing a study to try using data mining techniques using RapidMiner to find information or patterns regarding stroke from a dataset obtained from Kaggle. Three data mining techniques are used in this study, that is classification using decision trees, association rule using FP-Growth algorithm, and clustering technique using k-Means algorithm. Using RapidMiner, we are able to process the dataset using the operators provided in the application. As the result, we found out that due to an unbalanced data, the decision tree model made were only able to predict 68,75% patients as having stroke. With the association rule technique, we found out that most attributes in the dataset does not really associated with each other. With the clustering technique, we were able to group up patients and found out that most patients that have stroke are averaged in the age of 58, with 31 bmi, and 201 average glucose level.

Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan

To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.

Analysis of consumer characteristics on retail business with clustering analysis method and association rule for selling improvement strategy recommendations

In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.In the highly competitive retail industry, companies must continually innovate and develop unique business strategies to enhance their sales performance. The ABC Store, a mini market in Yogyakarta, has experienced fluctuating sales over the past year, failing to meet its targets. This study aims to analyze consumer purchasing behavior at the ABC Store and provide strategic recommendations to boost sales. The data analyzed in this study comprises three months of transaction records. The methods used include Association Rule - Market Basket Analysis (AR-MBA) with the FP-Growth algorithm and Clustering Analysis with K-Means. The clustering analysis identified four distinct customer segments: Mid-Morning Moderates, Diverse Afternoon Buyers, Evening Moderates, and High-Value Customers. Cluster 2, comprising Diverse Afternoon Buyers, was selected for AR analysis due to its relatively high transaction value and the variety of products purchased, indicating its potential to evolve into a High-Value Customers cluster. The analysis yielded 104 rules. The findings can inform marketing strategies to increase sales, including product bundling and customer loyalty programs such as a point system.

A machine learning-based data-driven method for risk analysis of marine accidents

In view of the frequent occurrence of marine accidents and the complex interaction of various risk-influencing factors (RIFs), a data-driven method to risk analysis that combines association rule mining (ARM) and complex network (CN) analysis is proposed in this study. The efficient FP-Growth algorithm is applied to facilitate ARM to examine risk patterns that frequently occur in marine accidents. Subsequently, CN theory is employed to scrutinise the multifaceted role of various RIFs and their interactions in the complex marine accident system, which involves the basic characteristics of the network, the identification of key RIFs through the application of the weighted LeaderRank (WLR) algorithm, and a robustness analysis. The results of the study indicate that compared with random networks, marine accident networks exhibit a higher level of complexity, which brings challenges to safety prevention and control. Inadequate regulation, violations, and deficiencies in safety management systems are identified as key RIFs, stressing the urgency of improving supervision, strengthening law enforcement and strengthening the safety management system. This study may facilitate maritime safety management of maritime traffic and the development of risk analysis methods.

Implementation of the Fp-Growth Algorithm on Spare Parts Supply Requests

Manufacturing companies rely on machines for operational activities to produce finished goods. Common factors constraining the demand and supply of spare parts are the high number of spare parts managed and irregular patterns of demand for spare parts. These varying quantities also require investment in spare parts inventory and longer response times than predicted. The research aims to apply the FP-Growth algorithm approach to find association rules and produce patterns of demand and supply of spare parts in lightweight brick manufacturing companies based on transaction data on demand and supply of spare parts from January – March 2023. The approach used is associated with the applied algorithm. In this research, the primary process of the FP-Growth algorithm is to create a combination of each item until no more combinations are formed using minimum support and minimum confidence parameters. Based on the results of making association rules using spare parts demand data from the machine maintenance department, it is stated that the regulations formed from processing the RapidMiner application with a confidence value of 100% recommend FD Regular Bolt spare parts, then the next rating with a confidence value of 94% is Steel Nuts, seven rules recommend Nuts. Steel. Therefore, it is recommended that FD Regular Bolts and Steel Nuts carry out safety stock to maintain stock availability and place them on shelves included in the fast-moving inventory category.

Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales

Analysis of Apriori and FP-Growth Algorithms for Market Basket Insights: A Case Study of The Bread Basket Bakery Sales

Temporal Association Rule Mining: Race-Based Patterns of Treatment-Adverse Events in Breast Cancer Patients Using SEER-Medicare Dataset.

Disparities in the screening, treatment, and survival of African American (AA) patients with breast cancer extend to adverse events experienced with systemic therapy. However, data are limited and difficult to obtain. We addressed this challenge by applying temporal association rule (TAR) mining using the SEER-Medicare dataset for differences in the association of specific adverse events (AEs) and treatments (TRs) for breast cancer between AA and White women. We considered two categories of cancer care providers and settings: practitioners providing care in the outpatient units of hospitals and institutions and private practitioners providing care in their offices. We considered women enrolled in the Medicare fee-for-service option at age 65 who qualified by age and not disability, who were diagnosed with breast cancer with attributed patient factors of age and race, marital status, comorbidities, prior malignancies, prior therapy, disease factors of stage, grade, and ER/PR and Her2 status and laterality. We included 141 HCPCS drug J codes for chemotherapy, biotherapy, and hormone therapy drugs, which we consolidated into 46 mechanistic categories and generated AE data. We consolidated AEs from ICD9 codes into 18 categories associated with breast cancer therapy. We applied TAR mining to determine associations between the 46 TR and 18 AE categories in the context of the patient categories outlined. We applied the spark.mllib implementation of the FPGrowth algorithm, a parallel version called PFP. We considered differences of at least one unit of lift as significant between groups. The model's results demonstrated a high overlap between the model's identified TR-AEs associated set and the actual set. Our results demonstrate that specific TR/AE associations are highly dependent on race, stage, and venue of care administration. Our data demonstrate the usefulness of this approach in identifying differences in the associations between TRs and AEs in different populations and serve as a reference for predicting the likelihood of AEs in different patient populations treated for breast cancer. Our novel approach using unsupervised learning enables the discovery of association rules while paying special attention to temporal information, resulting in greater predictive and descriptive power as a patient's health and life status change over time.

Application of Data Mining Techniques in Healthcare: Identifying Inter-Disease Relationships through Association Rule Mining

This research focuses on the application of data mining techniques in a healthcare environment by utilizing patient visit data from Hospital X, coded with ICD-10 diagnoses. The purpose of this study is to explore the application of data mining techniques in a healthcare environment, specifically to identify the relationship between diseases using patient visit data from X Hospital. This research utilizes the FP-Growth algorithm method followed by Association Rule Mining to find frequent occurrences of diseases in the data set. The research process involved data pre-processing, transformation into binary format, and careful parameter setting (minimum support 0.95 and confidence 0.9). The results showed a strong association between chronic conditions such as hypertension and diabetes, which are prevalent in the patient population. This association provides insight into potential comorbidities and may assist healthcare providers in improving diagnosis accuracy and treatment effectiveness. This research has implications for the application of data mining techniques, demonstrating its potential in improving predictive analytics in healthcare and strategic planning. This approach not only aids in the efficient allocation of healthcare resources, but also aligns with the broader goal of improving personalized patient care.

Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".

Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision

Inventory-based Business Strategy using FP-Growth at PT. Pinus Merah Abadi, Kendal Regency

This study aims to analyze inventory optimization using the Frequent Pattern Growth (FP Growth) algorithm, in relation to business strategy and/or accounting information systems, which a knowledge gap in previous research is as described above is the lack of knowledge about the application of the FP Growth algorithm in inventory optimization. This study was based on one year of sales and inventory data from a snack and beverage company, as a case study employing private data. A total of 3,370 transaction data samples were processed using RapidMiner data mining with FP growth algorithm. The results of both methods showed that the inventory level must be sufficient and produced two categories, namely large stock and small stock. The findings of this study indicated that the company has closely optimized its inventory since there were some products that were not involved in the association rules of sales.

Patterns Thematic Tracing of the Hadith of the Prophet Muhammad SAW Using the FP-Growth Algorithm and ECLAT

Patterns Thematic Tracing of the Hadith of the Prophet Muhammad SAW Using the FP-Growth Algorithm and ECLAT

Cryptocurrency Portfolio Management:A Clustering-Based Association Approach

The aim of this study is to identify crypto assets with similar characteristics and to explore the similar responses of these assets to market-priced events. This process is carried out in two stages. Cluster analysis and association analysis were applied in the research. First of all, cluster analysis was performed using the variables; the total number of active unique addresses, USD value of the current supply, fixed closing price of the asset, return on investment of the asset, total of the current supply, number of transactions, USD value of the sum of native units and 30 days volatility criteria. HK-Means algorithm and R Program were used for clustering. Then, the co-movement of crypto assets was analyzed using the FP-Growth algorithm and the WEKA program. 71 crypto assets with the highest market capitalization and meeting the research criteria were included in the research. The data used in the research covers the period of May 2021-May 2022. According to the main findings obtained from the research; within the framework of the criteria used in the research, 4 clusters were formed. Most important association rules found to be between; btc (bitcoin) & aave (nominex), eth (ethereum) & aave (nominex), dot (polkadot) & aave (nominex), neo & aave (nominex), uni (uniswap) & aave (nominex) , btg (bitcoin gold) & etc (ethereum classic), xrp (riple) & algo (algorand) & doge (dogecoin), xrp (riple) & doge (dogecoin), cro (cronos) & xrp (riple) & algo ( algorand) & trx (tron) & doge (dogecoin).

The development of association rules for student performance analysis using FP-Growth algorithm as a guideline for multidisciplinary learning

This study aims to develop association rules for student performance analysis using the FP-Growth algorithm. The data used for developing the association rules comprised 107 student reports. The reports, voluntarily provided by 107 junior high school students, consisted of student achievement results across 8 subject areas: Thai Language, Mathematics, Science, Social Studies, English Language, Computing Science, Visual Arts, and Home Economics. The data was applied to developing association rules using the FP-Growth algorithm towards WEKA, a machine learning software. The research team designed the process consisting of the following 5 stages: data collection, data preparation, model formulation, model evaluation, and model application. After achieving the association rules, the research team applied them to the prototype development of a student performance analysis system for promoting students' academic excellence. The system could be operated by Android mobile phones. According to the research results, the association rules developed by the algorithm provided a confidence level of 92%, and a rule of 7 rules will be generated. The findings indicated the correlations between the subject areas, which shared similar individual students' academic achievements (≥ 80 scores). The association rules could be applied to the multidisciplinary curriculum planning, which benefited students and promoted academic excellence. For example, by applying Rule, it could be assumed that students who earned 80 scores or higher in the English subject would likely earn identical scores from their Thai Language class. Therefore, they could effectively learn to integrate English and Thai languages. To illustrate, students may be asked to translate song lyrics from English to Thai, serve as tourist guides or translators, or even give welcome speeches to foreign guests.

EXPLORATORY ANALYSIS WITH ASSOCIATION RULE MINING ALGORITHMS IN THE RETAIL INDUSTRY

Every year the retail sector expands quickly. These industries are becoming more competitive and difficult to operate in due to their expansion. Changing consumer buying habits, a decline in people's spending capacity and an increase in international retailers are a few of the difficulties that must be overcome. In the context of mining frequent item sets, many methods have been proposed to push various kinds of limitations inside the most well-known algorithms. This study presents an exploratory analysis for retail stores that uses market basket analysis as one of the data mining techniques to identify frequent patterns in customer purchases. The proposed method is based on comparing two algorithms: Apriori and Frequent Pattern Growth (FP- Growth). The study used a retail store dataset consisting of 522,064 rows and 7 variables. Data pre-processing was performed to clean and encode the data to be used in the model. The dataset limitation involves 25% null values in the ID column. To address this, missing customer IDs are filled with the last valid ID, assuming repeated purchases. The FP-Growth algorithm was found to be faster and more effective than the Apriori algorithm in extracting frequent item sets and generating association rules. The retail industry based on these frequent item sets is expected to increase sales by recommending highly associated items to customers.

Optimization of Vegetable Restocking and Pricing Strategies for Innovating Supermarket Operations Utilizing a Combination of ARIMA, LSTM, and FP-Growth Algorithms

In the dynamic environment of fresh food supermarkets, managing the short shelf life and varying quality of vegetable products presents significant challenges. This study focuses on optimizing restocking and pricing strategies to maximize profits while accommodating the diverse and time-sensitive nature of vegetable sales. We analyze historical sales, pricing data, and loss rates of six vegetable categories in Supermarket A from 1 July 2020 to 30 June 2023. Using advanced data analysis techniques like K-means++ clustering, non-normal distribution assessments, Spearman correlation coefficients, and heat maps, we uncover significant correlations between vegetable categories and their sales patterns. The research further explores the implications of cost-plus pricing, revealing a notable relationship between pricing strategies and sales volumes. By employing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models, we forecast sales and determine optimal restocking volumes. Additionally, we use price elasticity theories and a comprehensive model to predict net profit changes, aiming to enhance profit margins by 47%. The study also addresses space constraints in supermarkets by proposing an effective assortment of salable items and individual product restocking plans, based on FP-Growth algorithm analysis and market demand. Our findings offer insightful strategies for sustainable and economic growth in the supermarket industry, demonstrating the impact of data-driven decision-making on operational efficiency and profitability.

Determination of association rules for mathematical skills

The aim of this study is to classify the subjects and skills of middle school mathematics course in the context of MATH Taxonomy and to determine their relations. For this purpose, the questions and answers related to the mathematics subtest of a national exam were analyzed over the answers of 20154 students. The study continued with the analysis of the data set containing the answer patterns of the students according to the rules of association of data mining methods by using RapidMiner program and FP-Growth algorithm. The results show that students with any level of skill in MATH taxonomy related to mathematical skills could perform skills in subcategories at a high rate. Students who have attainment in the learning areas provided later in the mathematics program had a high rate of knowledge in the previously provided learning areas. However, this result is not valid for Probability of Simple Events.