Discovering Association Rules Research Articles

Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research.

The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie's layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists' research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie's layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings.

Horizontal Learning Approach to Discover Association Rules

Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case).

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.

Analysis and Implementation of the Apriori Algorithm for Strategies to Increase Sales at Sakinah Mart

Sakinah Mart is a retail business that focuses on determining the layout of goods based on perceptions and implementing a discount system for specific items, but without offering bundling packages. This research aims to provide recommendations using the apriori algorithm as a decision-making tool for analyzing the layout of goods and bundling packages. The apriori algorithm is a data mining technique used to discover association rules and analyze customer purchases, specifically identifying the likelihood of customers buying item X along with item Y. The algorithm consists of two main components: support and confidence. The research applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, utilizing the apriori algorithm to analyze sales transaction data. The dataset includes 2000 sales transactions with two attributes, resulting in the identification of 2 and 3 itemsets. The findings include 16 rules with a minimum support value of 42% and a minimum confidence of 85% for the layout of goods. For bundling packages, 5 rules with a minimum support value of 40% and a minimum confidence of 90% were generated. These results offer valuable recommendations to the company, using the apriori algorithm for analyzing the layout of goods and bundling packages.

IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.

A comprehensive review of visualization methods for association rule mining: Taxonomy, challenges, open problems and future ideas

Association rule mining is intended for searching for the relationships between attributes in transaction databases. The whole process of rule discovery is very complex, and involves pre-processing techniques, a rule mining step, and post-processing, in which visualization is carried out. Visualization of discovered association rules is an essential step within the whole association rule mining pipeline, to enhance the understanding of users on the results of rule mining. Several association rule mining and visualization methods have been developed during the past decades. This review paper aims to create a literature review, identify the main techniques published in peer-reviewed literature, examine each method’s main features, and present the main applications in the field. Defining the future steps of this research area is another goal of this review paper.

Knowledge map construction based on association rule mining extending with interaction frequencies and knowledge tracking for rules cleaning

ABSTRACT Knowledge maps are teaching tools that can promote deeply learning and avoid knowledge loss by helping students plan learning paths. Mining potential association rules of concepts from student exercise data was a common method to construct knowledge maps automatically. While manual conditions should be set to filter the association rules future to improve the accuracy of knowledge maps, which made the construction of the knowledge map can not automatic totally. So, the study proposed a knowledge map construction method that combined knowledge tracking and association rule mining expanding with interaction frequencies based on exercise data to achieve rules cleaning automatically. The method first predicted students’ knowledge mastery sequences by a deep knowledge tracking model and discovered clustering relations to represent potential structures between concepts by fuzzy cluster analysis. Meanwhile, the method investigated association rule mining expanding with interaction frequencies to discover association rules between concepts. Finally, the clustering relations were used to filter the mined association rules automatically. To verify the effectiveness of the method, we constructed a knowledge map based on 34,350 online exercise data of 117 students in a computer programming course. Experimental results proved that the map was valid. Our implementations are available at https://github.com/xxdmw/FPGF-master.

Discovering Association Rules with Graph Patterns in Temporal Networks

Discovering regularities between entities in temporal graphs is vital for many real-world applications (e.g., social recommendation, emergency event detection, and cyberattack event detection). This paper proposes temporal graph association rules (TGARs) that extend traditional graph-pattern association rules in a static graph by incorporating the unique temporal information and constraints. We introduce quality measures (e.g., support, confidence, and diversification) to characterize meaningful TGARs that are useful and diversified. In addition, the proposed support metric is an upper bound for alternative metrics, allowing us to guarantee a superset of patterns. We extend conventional confidence measures in terms of maximal occurrences of TGARs. The diversification score strikes a balance between interestingness and diversity. Although the problem is NP-hard, we develop an effective discovery algorithm for TGARs that integrates TGARs generation and TGARs selection and shows that mining TGARs is feasible over a temporal graph. We propose pruning strategies to filter TGARs that have low support or cannot make top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> as early as possible. Moreover, we design an auxiliary data structure to prune the TGARs that do not meet the constraints during the TGARs generation process to avoid conducting repeated subgraph matching for each extension in the search space. We experimentally verify the effectiveness, efficiency, and scalability of our algorithms in discovering diversified top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> TGARs from temporal graphs in real-life applications.

Data Mining Tools for Generate Item Set : Critical Review

Most algorithms used to identify large itemsets can be classified as either sequential or parallel. In most cases, it is assumed that the itemsets are identified and stored in lexicographic order (based on item name). This ordering provides a logical manner in which itemsets can be generated and counted. This is the normal approach with sequential algorithms. On the other hand, parallel algorithms focus on how to parallelize the task of finding large itemsets. Mining Associations is one of the techniques involved in the process mentioned in chapter 1 and among the data mining problems it might be the most studied ones. Discovering association rules is at the heart of data mining. Mining for association rules between items in large database of sales transactions has been recognized as an important area of database research. These rules can be effectively used to uncover unknown relationships, producing results that can provide a basis for forecasting and decision making. Today, research work on association rules is motivated by an extensive range of application areas, such as banking, manufacturing, health care, and telecommunications. It is also used for building statistical thesaurus from the text databases, finding web access patterns from web log files, and also discovering associated images from huge sized image databases.

Cause analysis of construction safety accidents in China using association rules

In China the number of accidents and deaths in construction industry is increasing rapidly in recent years. Cause analysis is important to reveal the main factors contributing to the accidents, and therefore improve the safety mechanism for accident reduction in future. In this paper, 653 construction safety accidents in China which are fully investigated and formulated by a set of country-specific factors are used as the source data of potential causal relationships among the factors. By utilizing association rules a total of 13 main factors contributing to accident causation are explored, and 9 factors significantly impacting on the severity of accidents are identified. The discovered association rules offer scientific grounds for Chinese construction safety management and decision support. The novelty of this study is to present a different perspective during cause analysis in which the factors impacting the construction site safety and the consequences of construction accidents are revealed respectively.

Discovering Association Rules in Antimicrobial Resistance in Intensive Care Unit.

Multidrug resistant infections in intensive care units represent a worldwide problem with adverse health effects and negative economic implications. As artificial intelligence techniques are increasingly applied in diagnosing, treating, and preventing antimicrobial resistance, in this study, we explore the use of association rule mining in the antibiotic resistance profile of critically ill patients suffering from multidrug resistant infections.

Discovering association rules from big graphs

This paper tackles two challenges to discovery of graph rules. Existing discovery methods often (a) return an excessive number of rules, and (b) do not scale with large graphs given the intractability of the discovery problem. We propose an application-driven strategy to cut back rules and data that are irrelevant to users' interests, by training a machine learning (ML) model to identify data pertaining to a given application. Moreover, we introduce a sampling method to reduce a big graph G to a set H of small sample graphs. Given expected support and recall bounds, the method is able to deduce samples in H and mine rules from H to satisfy the bounds in the entire G . As proof of concept, we develop an algorithm to discover Graph Association Rules (GARs), which are a combination of graph patterns and attribute dependencies, and may embed ML classifiers as predicates. We show that the algorithm is parallelly scalable, i.e. , it guarantees to reduce runtime when more machines are used. We experimentally verify that the method is able to discover rules with recall above 91% when using sample ratio 10%, with speedup of 61 times.

A combinatorial optimization approach for multi-label associative classification

Mining associations between variables corresponding to multiple class labels (or outcomes) is prevalent in various applied domains, such as medical diagnosis, text mining, e-commence, and social behavior analysis. While most associative classification algorithms have been developed to discover association rules of binary variables for single-label classification problem, there are limited methods designed for the problem, called multi-label classification, that accounts for multi-labels. In this study, we consider the multi-label classification problem as a multi-class classification problem and formulate it as a 0–1 integer optimization model. We then leverage combinatorial optimization and association rule techniques to solve this hard problem. More specifically, we propose a ranking metric for selecting and aggregating stronger rules to form an optimal multi-label classifier. The computational results for multiple real applications show that our algorithm is able to identify significant association rules between key variables and multiple labels, and in turn achieves a competitive classification performance compared to state-of-the-art machine learning methods such as logistic regression, decision trees, and random forest. Moreover, we design a user-interface tool interfaced with the developed algorithm and demonstrate a medical diagnosis problem to predict multiple high-risk subgroups during emergency care in practice.

Federated Mining of Interesting Association Rules Over EHRs.

Federated learning has a great potential to create solutions working over different sources without data transfer. However current federated methods are not explainable nor auditable. In this paper we propose a Federated data mining method to discover association rules. More accurately, we define what we consider as interesting itemsets and propose an algorithm to obtain them. This approach facilitates the interoperability and reusability, and it is based on the accessibility to data. These properties are quite aligned with the FAIR principles.

Global Land Surface Dry/Wet Conditions Mining Based on Spatial‐Temporal Association Rules

AbstractSevere drought and wetness would have serious impacts on human society and natural environment. Discovering the implicit relationships among severely dry/wet conditions is of great significance for meteorological disaster early warning and risk management strategy formulation. In this study, we propose an improved spatial‐temporal association rule mining algorithm to mine the global dry/wet associations. A modified time‐lagged association rule mining algorithm is first developed to mine unit‐to‐unit dry/wet association rules. Then, a density‐based clustering algorithm is employed to identify associated dry/wet zones. The gridded Standardized Precipitation Evapotranspiration Index (SPEI) data sets at 12‐ and 3‐month time scales were used to characterize annual and seasonal dry/wet conditions. Analysis results show that there are strong dry/wet associations between many regions. Some of the discovered association rules reflect similar associations with known phenomena, indicating the effectiveness and feasibility of the method. Other rules were previously unknown and can provide new knowledge for this research field. Several predictions can be made according to the discovered rules that western Oman and southeastern Saudi Arabia would suffer severe or extreme drought in 2021 and northwestern China would suffer severe or extreme drought in 2025 with probabilities exceeding 77%; northwestern China, northern Argentina, and western Australia would experience severe or extreme wetness in 2021, 2022, and 2023, respectively, with probabilities exceeding 85%. The algorithm provides a new perspective for analyzing dry/wet associations and the discovered associations can be used as theoretical bases for local governments to take precautions to mitigate the potential impacts of severe drought or wetness.

Malicious URL Detection Based on Associative Classification.

Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates.

Deep mining of communication information association based on discrete Fourier transform

In order to better analyze communication information and improve the efficiency of communication information association mining, a discrete Fourier algorithm is proposed to extract association rules from information flow data with different time granularity. With the help of the MapReduce parallel programming model, a deep mining algorithm on the association of communication information is designed. Firstly, the data is linearly normalized, and the algorithm model of data depth mining is proposed according to the discrete Fourier transform. The data source uses the message middleware Kafka, and the intermediate results are stored in the memory database redis through the serialization technology. By using the structural information and comparison algorithm of the database, K pairs with the largest Pearson correlation coefficient are searched, association rules are mined, meaningless items are filtered out, construct an association deep mining model to realize rapid mining of association rules related to tasks. Experimental results and analysis show that the algorithm is efficient and stable in discovering association rules from high-speed information data flow, and supports different time granularity.

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.

An experimental approach for discovering association rules using FP-growth algorithm

An experimental approach for discovering association rules using FP-growth algorithm

A Survey on Frequent Item sets Mining for Big Data

connects large-volume, complex, and increasing data sets with multiple independent sources. Nowadays, Data are speedily expanding in all science and engineering domains due to the rapid evolution of data, data storage, and the networking collection capabilities. Due to its variability, volume, and velocity, Big Data enjoys the ability of extracting constructive information from huge streams of data or datasets. Data mining includes exploring and analyzing big quantities of data in order to locate different molds for big data. Frequent item sets is one of the most important tasks for discovering useful and meaningful patterns from large collections of data. Mining of association rules from frequent patterns from big data mining is of interest for many industries, for it can provide guidance in decision making processes; such as cross marketing, market basket analysis, promotion assortment, ...etc. The techniques of discovering association rules from data have traditionally focused on identifying the relationship between items predicting some aspect of human behavior; usually buying behavior. This paper provides a review on different techniques for mining frequent item sets.