FP-Growth Algorithm Research Articles

A statistical method for predicting quantitative variables in association rule mining

Background:Association rules encode common patterns and structures identified in datasets. They can be derived by association rule mining (ARM) algorithms. The association rules are human-readable and allow comprehensible predictions, unlike many other types of prediction algorithms. Objective:Classical ARM algorithms, like Apriori or FP-growth, cannot process interval or ratio scaled data (quantitative variables) which limits their applicability. Results:We address this restriction in classical ARM algorithms, making it possible to process quantitative variables on the right side of a rule. Our approach is based on applying the Kullback–Leibler divergence (KLD) to identify a rule which holistically considers complete data distributions instead of using only summary statistics.We demonstrate the new approach by using, among others, the example of predicting the length of stay of intensive care patients. The length of stay describes the number of days a patient spends in the intensive care unit. In addition, we further demonstrate our approach by predicting the credit score of bank customers and the contract duration of customers of a fictional telco company based on two publically available datasets. Conclusion:This paper shows a new approach for predicting quantitative variables in ARM. We demonstrate the new approach using the FP-growth algorithm.

Implementasi Data Mining Menggunakan Algoritma Fp-Growth Untuk Menganalisa Transaksi Penjualan Ekspor Online

In today's online technology, we have to learn various things so that we can adjust to social behavior, for example, buying and selling activities that cover all walks of life, now everyone doesn't hesitate to shop online. The online market is growing fast, especially in Indonesia. Anyone can sell and buy goods easily through online stores, so that people's purchasing power and sales increase due to the proliferation of online stores, XYZ stores are still beginners in terms of sales strategies so they try all efforts without careful preparation, one of the ways to do this is to expand its market coverage even to export goods abroad. the process of exporting goods in the current era is not a difficult thing to do, but it requires knowledge and preparation of a good strategy. from existing sales data can be analyzed to determine the right strategy in marketing goods. Data Mining is a science that helps analyze large amounts of data with the aim of gaining new knowledge that can be utilized. In Data Mining Analysis there is the FP-Growth algorithm which is a data mining technique for finding association rules from lots of data. This is known as the Association rule, where the rule will be determined from the minimum support and confidence results. the rules that are formed produce 10 rules out of 51 existing data, these rules help determine the export sales strategy at the XYZ store.

Application of Market Basket Analysis on Beauty Clinic to Increasing Customer’s Buying Decision

Beauty care and the need for cosmetics have become the lifestyle of modern women, especially in big cities. Public awareness to look beautiful makes modern women competing to take care of themselves to be more beautiful. The body care industry in Indonesia continues to grow. The growth has reached 6% and is predicted to continue to grow along with the high concern of Indonesian women in caring for their skin. To win the competition, companies need to know the market and consumer situation. One strategy that can be applied by the company is to use a promotional or advertising strategy. This research was conducted at the Ariana Audy beauty clinic in 2022 with the aim of identifying customer buying patterns which will then be used as reference material in the development of promotional menus for products and services offered by the beauty clinic. The approach used to design components on the promotional menu is Market Basket Analysis by applying the fp-growth algorithm using rapid miner software. Market basket analysis is focused on finding relationships between products based on customer purchases. The market basket analysis conducted resulted in 5 association rules that define consumer purchasing patterns for products and services provided by the Ariana Audy clinic. Through the 5 association rules formed, 3 promotional menus were produced, namely menu 1 consisting of baby skin crystal and oxy blue cream, menu 2 consisting of brightening cream products and sunscreen brightening, and menu 3 consisting of oxy jet peel and photodynamic therapy.

Penerapan Algoritma FP-Growth untuk Menentukan Strategi Promosi Berdasarkan Waktu dan Pembelian Produk

Sales is the main activity in every business. In making business decisions, sales patterns can be used to provide useful information such as strategies for promotion. Wandri Mart is a business engaged in the sale of products or goods commonly referred to as minimarkets in the city of Payakumbuh. In conducting promotional strategies, the owner of Wandri Mart does not know when to do promotions and what promotions are needed in order to increase sales. The purpose of this study is to obtain purchasing patterns related to the time of purchase and the type of goods purchased, so that a more effective promotional strategy can be developed. The method used by researchers is data mining techniques with the FP-Growth algorithm. The data used was taken as much as 5471 sales transaction data for 1 year. The results of this study indicate that the FP-Growth algorithm can be used to determine association rules using a minimum support of 1%, 2%, 3% and a minimum confidence of 10%. Experiments using Minimum Support 1% and Minimum Confidence 10% have the highest lift ratio value and produce more rules compared to other experiments so that it is obtained if on Tuesdays in August, customers buy instant noodles and packaged drinks with 6% and 5% support respectively and 50% and 45% confidence respectively with a lift ratio of 1.75 and 1.59 respectively. The lift ratio means that the rules have high association accuracy, and this also has a positive impact on sales and can be used as useful information for Wandri Mart to increase sales

Analisis Pola Asosiasi Data Transaksi Penjualan Minuman Menggunakan Algoritma FP-Growth dan Eclat

Every day transaction activities between companies and consumers continue to be carried out. This makes transaction data more and more and accumulate. This transaction data can be processed into more useful information using technology. Data mining is a technology that can work on a collection of transaction data into information that can be taken by companies as decision makers. The association rule method is used as a method to see the relationship between items in a transaction data. To analyze transaction data, researchers used the FP-Growth and Eclat algorithms. There are three stages of association in this study which are distinguished from the confidence value. The results in the first stage have a minimum confidence value of 0.4, the FP-Growth algorithm produces 41 association pattern rules, while the Eclat algorithm produces 32 association pattern rules. Then in the second stage the minimum trust value is 0.5, the FP-Growth algorithm produces 40 association pattern rules, for the Eclat algorithm it produces 32 association pattern rules. In the third stage, the minimum trust value is 0.6, the FP-Growth algorithm generates 32 association pattern rules, while the Eclat algorithm generates 30 association pattern rules. The results of the association pattern rules show that the Eclat algorithm is more efficient in determining the association pattern rules than the Fp-Growth algorithm

Market Basket Analysis dengan Perbandingan Metode Apriori dan FP-Growth Pada Data Transaksi XYZ

Technology is currently advancing quickly, allowing all organizations to grow their networks with its aid and create sales methods that now rely on technology to aid in making the proper judgments. When saved transaction data is accessible, every business will be able to implement its marketing strategy to maximize client transactions. use it to your advantage. Analysis of the market basket using the FP-Growth and a priori algorithm in transactions that aid in strategic planning and business product structuring. The FP-Growth algorithm and the Apriori algorithm work well together. One can evaluate the effectiveness of the employment of the a priori algorithm and the FP-Growth algorithm by applying both of them

Efficient Algorithms for Patterns Identification in Medical Data

Abstract Recently, medical databases have expanded rapidly, and the amount of information is huge. This abundance of data appears as a consequence of the new technologies that have been developed in the medical field and that allow easy data collection. The performance of the technique depends on the input data and available resources. Whereas, in Eclat the repeated scanning of the database is eliminated and consumes less time and we can conclude that Eclat is better than Apriori and Fpgrowth. If we refer to the execution time and memory usage, then the FP-Growth algorithm is more efficient than the Eclat algorithm or the Apriori algorithm. If we consider factor other than time, the result may vary from one factor to another.

Enhancing Sales Determination for Coffee Shop Packages through Associated Data Mining: Leveraging the FP-Growth Algorithm

The coffee shop business offers a diverse range of coffee and food options. However, customers often experience delays during transactions due to the extensive selection of menu items and combinations. This inconvenience not only discomforts new customers but also hampers their likelihood of returning, potentially impacting the overall business turnover. To address this issue, this study aims to establish association rules by combining the least and most popular menu items for the upcoming month. These rules will serve as a guideline for creating shopping packages that streamline the decision-making process. The FP-Growth algorithm is employed to analyze sales transaction data from January to March 2023, comprising 2,336 transactions in .csv format. Among the generated association rules, two rules stand out with the highest support and confidence values. The first rule exhibits a support value of 0.3% and a confidence of 70.0%, while the second rule showcases a support value of 0.4% and a confidence of 69.2%. By considering these two rules alongside the existing menu options, coffee shop owners can effectively curate shopping packages that cater to customer preferences. It is anticipated that these packages will elevate the quality of service, attract a greater number of customers, and subsequently enhance the overall business turnover.

Perancangan Tata Letak Toko Ritel Berdasarkan Pola Belanja Konsumen Dengan Market Basket Analysis (Studi Kasus: Indomaret Sukatani)

<p><strong>During the pandemic in early 2020, consumer behavior in shopping at retail stores changed. Some of the main factors taken into consideration that can influence consumer shopping behavior are facilities, layout, and time spent shopping. This can affect product sales at Indomaret Sukatani which has an ineffective layout so that it is difficult for customers to reach and customers need to spend a long-time shopping. To solve these problems, it is necessary to do data mining using the Market Basket Analysis method with the Apriorist and FP-Growth Algorithm so that customer shopping patterns can be known in order to design new layouts. Then the effectiveness test can be done by calculating the rectilinear distance to find out the most optimal layout design, namely the layout design based on the FP-Growth Algorithm with the smallest total rectilinear distance value of 2353.5 in the first scenario, 2313 in the second scenario, and 1609.5 in the third scenario.</strong></p><p><strong><em>Keywords</em></strong> – <em>Customer Behaviour, Layout, Market Basket Analysis</em></p>

MBA: Market Basket Analysis Using Frequent Pattern Mining Techniques

This Market Basket Analysis (MBA) is a data mining technique that uses frequent pattern mining algorithms to discover patterns of co-occurrence among items that are frequently purchased together. It is commonly used in retail and e-commerce businesses to generate association rules that describe the relationships between different items, and to make recommendations to customers based on their previous purchases. MBA is a powerful tool for identifying patterns of co-occurrence and generating insights that can improve sales and marketing strategies. Although a numerous works has been carried out to handle the computational cost for discovering the frequent itemsets, but it still needs more exploration and developments. In this paper, we introduce an efficient Bitwise-Based data structure technique for mining frequent pattern in large-scale databases. The algorithm scans the original database once, using the Bitwise-Based data representations as well as vertical database layout, compared to the well-known Apriori and FP-Growth algorithm. Bitwise-Based technique enhance the problems of multiple passes over the original database, hence, minimizes the execution time. Extensive experiments have been carried out to validate our technique, which outperform Apriori, Éclat, FP-growth, and H-mine in terms of execution time for Market Basket Analysis.

Implementasi Data Mining Pada Penjualan Pakaian dengan Algoritma FP-Growth

The amount of competition in the business world, especially in the sales industry, required developers to find a strategy that could increase sales and marketing of products sold, one of which was using clothing sales data with data mining. Data Mining was an iterative and interactive process to find new patterns or models that can be generalized for the future, valuable, and understandable in a massive database. HAS Stores in the arrangement of goods layout still place goods according to groups and types of goods, so that it has an impact on service and item search when consumers want to buy more than one item and are located far apart. Therefore, this study aims to apply the FP-Growth Algorithm to find out the most sold clothing sales at HAS Stores in Batam city. This study uses the Association Rule method by utilizing the FP-Growth Algorithm. This study aimed to apply the FP-Growth Algorithm to determine the most sold clothing sales at HAS Stores in Batam city. Through the mining process with the FP-Growth (Frequent Pattern Growth) algorithm, the types of clothes sold will be obtained, and how much inventory the store needs to provide the clothing stock. The results showed that the most sold clothing products were Gamis and Jilbab through the calculation of support 53,33% and confidence 100%. Regarding these results, marketing strategies can be focused on the product and set a layout that customers can easily see.

Efficient False Positive Control Algorithms in Big Data Mining

The typical hypothesis testing issue in statistical analysis is determining whether a pattern is significantly associated with a specific class label. This usually leads to highly challenging multiple-hypothesis testing problems in big data mining scenarios, as millions or billions of hypothesis tests in large-scale exploratory data analysis can result in a large number of false positive results. The permutation testing-based FWER control method (PFWER) is theoretically effective in dealing with multiple hypothesis testing issues. In reality, however, this theoretical approach confronts a serious computational efficiency problem. It takes an extremely long time to compute an appropriate FWER false positive control threshold using PFWER, which is almost impossible to achieve in a reasonable amount of time using human effort on medium- or large-scale data. Although some methods for improving the efficiency of the FWER false positive control threshold calculation have been proposed, most of them are stand-alone, and there is still a lot of space for efficiency improvement. To address this problem, this paper proposes a distributed PFWER false-positive threshold calculation method for large-scale data. The computational effectiveness increases significantly when compared to the current approaches. The FP-growth algorithm is used first for pattern mining, and the mining process reduces the computation of invalid patterns by using pruning operations and index optimization for merging patterns with index transactions. The distributed computing technique is introduced on this basis, and the constructed FP tree is decomposed into a set of subtrees, each corresponding to a subtask. All subtrees (subtasks) are distributed to different computing nodes. Each node independently calculates the local significance threshold according to the designated subtasks. Finally, all local results are aggregated to compute the FWER false positive control threshold, which is completely consistent with the theoretical result. A series of experimental findings on 11 real-world datasets demonstrate that the distributed algorithm proposed in this paper can significantly improve the computation efficiency of PFWER while ensuring its theoretical accuracy.

Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu Recommendations

Existing transaction data is only recorded and stored as a sales transaction memorandum, so it has not been utilized optimally. The data is only stored and used as transaction history. The availability of a lot of data and having a pattern of sales transactions that are similar to MSME Cafe Over Limit will be utilized by using data mining science. This research uses the association rules method. Implementation of fp-growth to get item combinations. The purpose of this research is to make it easier for MSMEs to determine menu recommendations for customers. The fp-growth algorithm is used to process as many as 2038 transaction data with a minimum support value of 10%, while for a minimum confidence value of 50%. So that there are 3 rules, namely "if you order Mariam chocolate cheese milk then the customer will order Kopsus Overlimit", from this rule it will form a support value of 10.79%, using a confidence value of 54.19% and a lift ratio of 0.93. Furthermore "if you order Kopsus Overlimit then you will order tofu at grandma's house", from the rule it will produce a support value of 34.69%, with a specified confidence value of 59.76%, so the lift ratio value is 1.15. The last rule "if you order tofu at grandma's house, the customer orders Kopsus Overlimit", from the rule that occurs, the support value is 34.69%, with a confidence value of 66.7% and a lift ratio of 1.15. The results of the study found the two best rules, namely "if the customer orders over-limit Kopsus, he will order tofu at grandma's house" and "if he orders tofu at grandma's house, the customer orders over-limit Kopsus". Based on the results of the rules formed, it can be concluded that only two rules can be categorized as valid and can be used as a reference in food and beverage menu recommendations at MSME Cafe Over Limit. So the results of this study can be useful to be applied to MSMEs, especially in terms of menu recommendations.

Machine Learning Prediction Models applied to Weather Forecasting: A survey

Using scientific knowledge, weather forecasters can predict what the atmosphere will be like in a particular place. It predicts snow, cloud cover, rain, temperature and wind speed. Since weather predictions consist of multidimensional and nonlinear data, they are one of the world's most challenging problems. Various machine learning algorithms and methods have been used in data mining for weather prediction, including Support Vector Machines, supervised and unsupervised machine learning algorithms, artificial neural networks, FPGrowth Algorithms, Hadoop with Map Reduce, K-medoids, and Naive Bayes. This survey briefly explains the methods used to build weather forecasting models to assist researchers in choosing the appropriate method for their model.

Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data

This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.

Implementasi Data Mining FP-Growth Untuk Analisis Pola Pembelian Pada Transaksi Penjualan

In the business world, efforts are needed as much as possible in gaining profits. The accuracy of marketing strategies can be seen from the consumer spending pattern database obtained from sales transactions on fashion products that are usually purchased simultaneously by customers. Information about the Pattern of Purchasing Customer Shopping that is Inaccurate at the Ayu Collection Online Shop Shop has caused promotional policy to be one of the causes of the store to suffer losses. One way to get an accurate customer shopping pattern is to use data mining. One of the methods contained in data mining is the association analysis method, in the association analysis there are several algorithms, one of which is the FP-Growth algorithm. In this study several association rules were found by applying the Frequent Pattern (FP-Growth) algorithm from the transaction database Fashion sales at Ayu Collection Online Shop. This association rules will later be used as decision making material to develop successful marketing and sales strategies. The findings of this study are in the form of product recommendations, namely the proposal of two or more items based on the findings of the FP-Growth algorithm using a 50% confidence value and a minimum support of 40%, this study uses assistance from the rapidminer tools version 9.9.

Association rules and prediction of transportation mode choice: Application to national travel survey data

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.

Frequent Itemset Mining Algorithm Based on Linear Table

Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number of scans to the original dataset. Furthermore, operations such as pruning and grouping are also used to optimize the algorithm. For different datasets, the algorithm shows different mining speeds. (1) In sparse datasets, the algorithm achieves an average 45% improvement in mining speed over the bit combination algorithm, and a 2-3 times improvement for the classic FP-growth algorithm. (2) In dense datasets, the average improvement over the classic FP-growth algorithm is 50-70%. For the bit combination algorithm, there are dozens of times of improvement. In fact, the algorithm that integrates bit combinations with bitwise AND operation can effectively avoid recursive operations and it is beneficial to the parallelization. Further analysis shows that the linear table is easy to split to facilitate the data batch mining processing.

Android malware detection based on sensitive patterns

In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry. However, in order to improve the accuracy of detection, the existing static detecting methods sacrifice the excessively high analysis complexity and time cost. Moreover, the correlation between static features leads to redundancy of a large amount of data. Therefore, this paper proposes a static detecting method of Android malware based on sensitive pattern. It uses an improved FP-growth algorithm to mine frequent combinations of sensitive permissions and API calls in malicious apps and benign apps, which avoids the generation of redundant information. In addition, this paper adopts multi-layered gradient boosting decision trees algorithm to train the detection model. And a dual similarity combination method is proposed to measure the similarity between different sensitive patterns. The experimental results show that our proposed detection method has high accuracy and great generalization ability.

Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).