Generate Association Rules Research Articles

Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

Influence factors on travel mode preference of working residents living far away from downtown area on workdays: A hybrid method integrating random parameter logit model and Apriori algorithm

This study presents a detailed analysis on the characteristics of travel mode preference of working residents living far away from downtown area on workdays, using GPS-based activity travel diary data from Shangdi area (Beijing). A hybrid method integrating random parameter logit model with systematic heterogeneity (RPL-SH) and Apriori algorithm is put forward to explore the influence factors and interaction effects affecting travel mode preference. First, the RPL-SH model is established to explore significant factors, and capture the unobserved random heterogeneity and systematic heterogeneity due to individual characteristics on the travel mode preference. Then, these significant factors are used to generate association rules by Apriori algorithm to investigate statistical associations between the specific travel mode preference and these significant factors. Ten significant factors are found in the RPL-SH model, in which annual household income is normally distributed. The results of the Apriori algorithm indicate that some factors combined with other factors could significantly influence working residents’ travel mode preference. For example, the combination of lower annual household income and shorter distance between workplace and the nearest bus stop is highly associated with green travel mode preference. Moreover, the results show that the proposed hybrid method not only demonstrates the consistency of the results of the two methods, but also plays a complementary role in exploring more information on travel mode preference. This research hopes to give regulators a better understanding on how working residents living far away from downtown area choose their travel mode, so as to develop more effective and targeted measures for reducing private car use and alleviating workday traffic congestion.

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.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

— This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

МЕТОД ПОШУКУ ЗАЛЕЖНОСТЕЙ У ВЕЛИКИХ НАБОРАХ ДАНИХ ІЗ ПРОПУСКАМИ НА ПРИКЛАДІ ДАНИХ ПРО ПОШИРЕННЯ COVID

In the article, a method of big data analysis with omissions is developed on the example of the construction of advisory rules regarding adequate state policy to reduce the spread of new Covid-19 cases. Association rules and rule generation in Big Data have a number of challenges, the main one being the presence of large numbers of vectors and multivalued datasets. That is why in the paper the system of these rules is based on a new ensemble of machine learning techniques such as associative rules, regression tree and clustering. This study used pooled data from the Government's COVID-19 Response Tracker and ECDC's Covid-19. Clustering was performed using the k-means method. Gap statistics allow finding an appropriate number of clusters, and in the case study three clusters were selected. The clusters differ in the recommendations and actions of the correspondent governments. Thus, the first countries of the cluster chose to close schools and control international travel as the main recommendations; countries in the second cluster recommended staying at home, while the main recommendations from the governments of countries in the third cluster were to stay at home and cancel public events. The same country can be attached to different clusters at different time intervals. Therefore, the clustering by countries will not be so unambiguous. That is why the time series for the detached country can be interesting and will be the subject of further study. In addition, the impact of such clustering on the spread of COVID-19, the position and duration of the peak, and the mortality rate will also be the subject of our further investigation. A regression decision-making tree was built, a set of rules was obtained from the decision tree and applied rules for generating associative dependencies. The resulting dependencies can be used for strategic planning in the healthcare system.

Deciphering Patterns in a small scale Case Analysis Study of the Apriori Algorithm in Market Basket Analysis using machine learning tools

Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis

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.

Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

ShopSculpt : Crafting Your Cart with Intelligent Ingenuity

In the present world from small companies to giant company’s product recommendation system plays a very major role, also people are very much interested in online shopping these days, so Recommendations are utilized to make the customer's job easier and faster. The majority of these recommendations are made based on their previous transaction history and association rules generated from it. Apriori Algorithm is the most widely used method for generating association rules based on frequent product sets. But it has a drawback, while generating association rules it uses the whole transactions list and frequent product sets, ignoring the seasonal transactions for generating rules. Hence seasonal transactions are not considered for mining rules, if a person is buying a cake for Christmas, it’s very likely that recommender system recommends birthday balloons, birthday caps as recommendations. But actually, it should recommend Christmas related products for recommendation. To solve this problem, we proposed a model using chi square test with improvised Apriori algorithm. This research paper introduces ShopSculpt, an innovative automated shopping cart system designed to enhance the user shopping experience by leveraging intelligent algorithms. ShopSculpt employs a multifaceted approach, considering user shopping interests, patterns, seasonal trends, and current weather conditions to provide personalized and context-aware product recommendations. The aim is to create a dynamic and responsive shopping environment that adapts to individual preferences and external factors, thereby optimizing the user's shopping journey.

User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilized for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.

Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique

<abstract><p>Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.</p></abstract>

RANCANG BANGUN APLIKASI DATA MINING DENGAN ALGORITMA FP-GROWTH PADA DATA PENJUALAN SPAREPART MOBIL SUZUKI RADIO DALAM

Suzuki Radio Dalam is an automotive company operating in the automotive sector. They have been facing a challenge where sales data of spare parts has been accumulating without being effectively utilized or managed. The company has never employed data mining techniques to extract meaningful patterns or insights from this spare parts sales data. To address these issues, the researchers adopted the data mining technique known as the FP-Growth algorithm. The FP-Growth algorithm is a form of association algorithm within data mining. Association algorithms are utilized to uncover relationships and connections between variables present in a dataset. Through the application of the FP-Growth algorithm, data can be extracted through the construction of FP-Trees, which reveals insights into patterns of items purchased by customers. This method allows for the identification of frequently co-purchased items, enabling the company to devise marketing strategies aimed at boosting spare parts sales. The proposed solution involves creating a web-based platform to facilitate the FP-Growth algorithm calculations, particularly when dealing with large volumes of data. This web-based system was developed using PHP and utilizes a MySQL database. This data is then subjected to FP-Growth algorithm calculations and subsequently analyzed to generate association rules. These association rules hold valuable information about customer purchasing patterns. The implementation of this web mining solution streamlines the FP-Growth algorithm calculations, making it more manageable and efficient when dealing with substantial datasets. The resulting association rules derived from these calculations provide actionable insights for Suzuki's marketing strategies. By offering enticing promotions to customers based on the information gleaned from these association rules, the company aims to enhance spare parts sales.

Enhancing Marketing Strategies for Office Stationery Stores Using Equivalence Class Transformation Algorithms

Office stationery stores facilitate operations for offices, schools, and businesses. However, the store owner must overcome the challenge of selecting the right products for promotion. This research aims to tackle this issue by comparing the performance of the Equivalence Class Transformation algorithms in discovering association rules using Support, confidence, and lift ratios. The findings reveal that the algorithm generates association rules based on Support, confidence, and lift values. Ten critical rules are identified, shedding light on algorithm effectiveness. Ultimately, this study underscores the significance of refining marketing approaches for brick-and-mortar stationery businesses and the value of data-driven methods in aiding decision-making. In the early promotion catalog phase, priority is assigned to rules with solid agreement by the Shop Owner, guiding the selection of featured items based on robust product associations.

Studying the impact of building morphology on occupants’ movement using a rule mining approach

In the context of mitigating viral transmission, particularly the case of COVID-19, engineering controls have proven to be highly effective. A critical facet of bolstering the efficacy of engineering controls lies in exploring the correlation between the movements of occupants and the building morphology. While extant investigations have predominantly centered on scenarios involving emergency evacuations or day-to-day wayfinding, a knowledge gap exists in our understanding of how occupants’ movements relate to building morphology.To bridge this gap and furnish a more robust foundation for engineering controls during pandemics, this paper introduces a data-driven methodology tailored to studying this relationship both pre-pandemic and during pandemics. The methodology hinges on two primary datasets: building features and the occupants' movements. Initially, building morphology is characterized by discerning pertinent architectural features distilled from prior research. The tracking of occupants’ movements is achieved through a thorough analysis of occupancy and trajectory data. Subsequently, Association Rule Mining (ARM) is used to investigate meaningful relationships. These generated association rules are subsequently elucidated using the proposed interpretation approach.The study's outcomes underscore the pivotal role played by certain building features, most notably floor size, floor level, and room functions, in shaping patterns of occupancy and transit. Moreover, a comparative analysis of occupants' movements before and during the COVID-19 pandemic demonstrates the tangible benefits of engineering control measures. The research contribution unveils the relationship between occupants and building morphology. It proposes a baseline for conducting investigations on relationships between buildings and occupants. Beyond pandemic preparedness, the implications of this research extend to building design, operation, and management.

Coupled risk analysis of hospital infection: A multimethod-fusion model combining association rules with complex networks

During the initial phase of COVID-19 pandemic, hospital infection (HI) accidents occurred with alarming frequency. Proactively preventing and controlling HI risks is particularly challenging as hospital is a complex and dynamic scene that involves multiple uncertainties and coupled risks. Thus, this paper develops a multimethod fusion model that combines association rule mining with complex networks to systematically analyze coupled HI risks. Firstly, risk factors of HI are obtained and categorized based on real data in hospitals. An improved Apriori algorithm is used to mine coupled relations between risk factors and generate association rules. Then, complex networks of HI risk factors are constructed based on existing rules. Through an in-depth analysis of the networks’ topology and statistics properties, this study reveals that HI risk networks follow a power-law distribution, attesting to the non-random nature of risks. The proposed approach is applied to analyze HI risks during COVID-19 outbreak in a tertiary hospital in Wuhan. Results show that the method can successfully reveal the coupling of risks and identify key risks of HI. Effective responses can be undertaken in advance to prevent identified HI risks, thereby providing valuable guidance for future hospital management.

The Effect of Social Media on Student Learning Motivation Using the Apriori Method

The success of student learning can be determined by their motivation. Students who have high learning motivation tend to have high achievement as well, otherwise their learning motivation is low, their learning achievement will also be low. student learning motivation in the subject is very low. Some students prefer to play social media rather than pay attention to the material explained by the teacher during class hours. Therefore, this study aims to explore the influence of social media on students' learning motivation. This research uses data mining method with Apriori algorithm to identify patterns related to social media usage and students' learning motivation. The Apriori algorithm is one of many algorithms in data mining that is used for frequent itemsets and association rules in databases on transactional data that are generated by identifying each item that exists, and combining larger sets of items provided that the items appear frequently enough in the database. Based on the research that has been done, the author can draw the conclusion that using the Rapid Miner 7.1 application tools in applying the apriori algorithm produces the same rules as manual calculations using 300 data on the learning motivation of Abdi Negara Binjai SMKS students and the system can generate association rules using 300 student learning motivation data with a minimum support of 12% and a minimum confidence of 75% and produce 5 association rules 3 itemsets to determine the learning motivation of Abdi Negara Binjai SMKS students. One of the rules that has the highest confidence value is, if YT and J2 then M1. Which means that every student who uses YOUTUBE Social Media with a length of use is 3-4 HOURS then INCREASES STUDY MOTIVATION. Then the less the ɸ (frequent) value is set, the more data that can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.

Efficient mining of concise and informative representations of frequent high utility itemsets

The discovery of frequent closed high utility itemsets (FCHUIs) and frequent generators of high utility itemsets (FGHUIs) is significant because they serve as important and concise representations of frequent high utility itemsets (FHUIs), offering a brief but essential summary that can be considerably smaller. Besides, they facilitate the generation of nonredundant high utility association rules that are crucial for decision-makers. However, the challenge lies in the difficulty of mining these representations due to scalability issues, high memory usage, and long runtimes, particularly when dealing with dense and large datasets. To address this issue, this paper proposes a novel approach for efficiently mining FCHUIs and FGHUIs using a novel weak lower bound named wlbu on the utility. The approach includes effective pruning strategies for early eliminating non-closed and/or non-generator high utility branches in the prefix search tree based on wlbu. These pruning strategies allow faster execution with lower memory usage. In addition, the paper presents two novel algorithms, FCGHUI-Miner and FGHUI-Miner, which can simultaneously discover both FGHUIs and FCHUIs or solely mine FGHUIs, respectively. The experimental results demonstrate that the proposed algorithms outperform state-of-the-art algorithms in terms of efficiency and effectiveness.

A Data Mining Framework for Maintenance Prediction of Faulty Products Under Warranty

Predicting the sequential patterns of maintenance activities (replacement or repair) with the needed spare parts for faulty products becomes the main challenge to maintenance engineers. This research, therefore, develops a data mining framework for predicting the integrated sequential patterns of maintenance activities and identifying the classification of the frequent components’ spare parts for faulty products under warranty. In this framework, a large data set was mined for products under warranty including product attributes, maintenance activities, and spare parts. Then, data mining techniques were performed to determine the frequent sequence pattern of maintenance activities, involving: the selection of monthly maintenance activities, generalized sequential patterns (GSP), generation of association rules, and rule-based classification with/without considering product attributes. The frequent sequential patterns were validated using testing data. Further, the GSP was applied to determine the frequent sequential patterns of spare parts. Finally, integrated sequential patterns were generated for maintenance activities and spare parts. A case study of water-cooled chiller products was deployed to illustrate the developed framework. The effectiveness of the framework was illustrated with the warranty data mining for a water-cooled chiller. In conclusion, the proposed framework allows maintenance engineers to extract hidden knowledge regarding sequential patterns of maintenance planning and provides valuable information for maintenance prediction.

Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data

Culture Coffee MSME is one of the MSMEs engaged in the culinary field and is experiencing business competition. A marketing strategy is needed with the right decision-making process so that the business can survive and excel. UMKM Culture Coffee uses a point of sales application to accommodate the transaction process and record transactions. Historical customer data can be processed into a basis for decision making for marketing strategies that effectively increase sales. However, the transaction data has not been used optimally. There is a need to analyze historical customer data that can generate information to form marketing strategies. Market Basket Analysis (MBA) is one of the methods in data mining used in knowing products that tend to be purchased together by customers known as Association Rule. Association rules produce products in the form of packages or bundling which are used as marketing strategies. The marketing strategy obtained is supported by data visualization which contains information from the data. Apriori algorithm is used to generate association rules. The result of this research is an association rule on the historical data of MSME Culture Coffee customer purchases. Based on these rules, recommendations for selling menu packages to customers can be given. The purpose of this research is to find customer purchasing patterns which are used as the basis for decision making in determining menu sales. The results showed 2 product packages, namely, nuggets and french fries with sausages and french fries with a support and confidence value of 12.5% and 37.6% with 10.8% and 29% respectively. The results of this study can be used as a basis for the sales and marketing strategy of Culture Coffee MSMEs to increase business revenue.