Data Mining Association Rules Research Articles

Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm

As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.

Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30

With the increased financial fragility, methods have been needed to predict financial data effectively. In this study, two leading data mining technologies, classification analysis and association rule mining, are implemented for modeling potentially successful and risky stocks on the BIST 30 index and BIST 100 Index based on the key variables of index name, index value, and stock price. Classification and Regression Tree (CART) is used for classification, and Apriori is applied for association analysis. The study data set covered monthly closing values during 2013-2019. The Apriori algorithm also obtained almost all of the classification rules generated with the CART algorithm. Validated by two promising data mining techniques, proposed rules guide decision-makers in their investment decisions. By providing early warning signals of risky stocks, these rules can be used to minimize risk levels and protect decision-makers from making risky decisions.

APPLICATION OF DATA MINING BY USING A PRIORI ALGORITHM TO IMPROVE CUSTOMER PURCHASING DECISIONS AT MIKAMART BLITAR STORE

Product arrangement, known as product display, is the layout or way of structuring products, especially products companies apply to attract consumers. Although one of the scopes of sales in the market is the delivery of information through galleries, storefronts require specialization and the ability to arrange products or displays following the standards and specifications of the company and the product itself. The resulting output directly affects the level of sales and information to be achieved in a store, especially in modern retail stores with a self-service format (self-service) such as minimarkets. To implement a display system, Data Mining is applied as a determinant of the layout of the merchandise. According to (Kusrini & Lutfi 2009), data mining is used to extract knowledge from databases. One of the many methods in Data Mining is the Apriori Algorithm. The a priori algorithm is a type of association rule in data mining. Association rule or association rule mining is used to find associative rules between item combinations to be used as wisdom to make decisions and provide a stock of products that consumers prefer

Emerging Associates of the Circular Economy: Analysing Interactions and Trends by a Mixed Methods Systematic Review

Circular Economy (CE) frameworks augmented multi-dimensional research, including definitions, principles, indicators, history, limitations, enablers, and allied issues. Rethinking the designing strategies requires the exploration of CE associates and their functioning as companions for amplification through multiple suggestions, highlighting the interactions with the overall CE system towards sustainability. This paper focuses on how CE literature concentrates on premeditated areas and solution approaches. A lack of deterministic manifestations critically limits the interpretation and application of CE concepts. The paper contributes to the CE theoretical and practical understanding by for the first time outlining the CE associates, their connotation, progression, positioning in architecture, and their current state. These associates support individualization of a CE concept. The coherently applied methodology attempts to reduce the knowledge gap by following a systematic quantitative literature review process to screen 76 articles and 122 CE definitions, referencing one decade of literature. Application of comprehensive morphological mining facilitated the extraction of CE associates and later analysis. On application of Bibliometric Analysis, Text Analysis, Semantic Analysis, and Relation Mapping to the literature, 49 associates emerged. They were further validated by application of Content Analysis and Weighted Average Priority Ranking on extracted article content and definitions for deriving the most prominent associates. Subsequently, Association Rules Data Mining and K-Means Clustering were deployed in exploring the relations, ranking, and significance of CE associates. The findings consistently state that business actions (virtualize, exchange); sustainability pillars (society); and R strategies (refuse, refill, and repurpose) are unexplored CE associates, whereas Business, Economics, Environment, Measurement, Product, Industry, Resource, System, Waste, and Recycle are the most explored associates of the CE ecosystem.

Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart cities

AbstractThere has been an exponential growth in the number of low‐cost heterogeneous sensor devices that are connected to the internet in the existing infrastructure of smart cities in the past decade. These sensors and actuator devices are employed in various industries to collect invaluable data that greatly impacts business decisions. State‐of‐the‐art research is being carried out to process the high‐volume data collected in high velocity streams with high variability in order to draw meaningful insights to cater to business needs in various domains. Big data analytics finds diversified opportunities in 5G‐enabled Internet of Things (IoT) and industrial IoT environment to study the data patterns and produce new results which provides big organizations a conducive environment to take informed decisions. This article presents an exhaustive investigation of the various applications and algorithms of the big data analytics in 5G‐driven IoT and industrial IoT systems with a detailed taxonomy of the existing analytical systems and also the challenges specific to the applications in an IoT environment. A holistic understanding on the importance of security and privacy of big data during the development of smart city infrastructure in high velocity streaming process has been explained for a broad understanding of the big data streaming processes. A thorough analysis of important data mining algorithms such as classification, association rule mining, clustering, and prediction methods for big IoT data from the recent literature also has been presented to provide a clear understanding of existing issues in handling big data in 5G‐enabled IoT for the development of sustainable smart city infrastructure.

Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining

The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.

Safety warning of coal mining face based on big data association rule mining

The hidden danger of accidents seriously threatens the safety of the working face of coal mining. By digging the internal association rules of hidden danger, the construction of safety warning rule base can improve the ability of coal mining enterprises to deal with hidden danger as soon as possible. Coal mining work face hazards are analyzed with three types of hazard theory, and the types, attributes and dimensions of hidden accidents in coal mining face are studied, combined with the production practice of coal mining working face, and referring to the thought and method of “5W1H”. Then, the structure of safety early warning knowledge base is studied and the key of safety early warning knowledge base is put forward. On this basis, the paper designs the mining model of mining face data to mine the association rules of accident hidden danger data. The empirical analysis shows that the rule base of safety warning can be used to deal with hidden danger of coal mining working face, and realize the early warning of hidden danger of accident, providing a method for dealing with hidden danger of coal mine accident and managing dangerous source.

Research on the Relevance of Art Courses in Colleges and Universities Based on Data Mining

With the development of educational informatization, the educational data of each school is increasing day by day. How to rationally use the existing information to make scientific teaching decisions is a problem that every educator is closely concerned about. This paper proposes a correlation analysis method for college art courses based on data mining technology. Through the study of association rules in data mining, the Apriori algorithm based on three-dimensional matrix is used to quickly mine student performance data, so as to obtain some reasonable and reliable courses. The results show that the method in this paper can find valuable information for curriculum setting from the actual teaching data, so as to reasonably optimize the curriculum, provide a decision-making basis for the revision of the art curriculum and syllabus in colleges and universities, and further improve the teaching effect and the quality of personnel training.

ASOSIASI RULE MINING UNTUK REKOMENDASI PADA TRANSAKSI PEMINJAMAN BUKU MENGGUNAKAN FREQUENT PATTERN

The purpose of this study is to provide recommendations for grouping books at the ITB STIKOM Bali library, Jimbaran Campus. Currently, the placement of books in the ITB STIKOM Bali library, Jimbaran Campus, is still using a manual catalog. To make it easier for staff to manage books in the library, especially in terms of book data that can be borrowed, recommendations for grouping and placing books in the library are made based on the data on books that are most often borrowed together by library visitors obtained from previous borrowing data. This is done by data mining using the frequent pattern method. The use of this frequent pattern method is applied to the borrowing data of the ITB STIKOM Bali library at Jimbaran campus so that information or knowledge is obtained about recommendations for placing books that will be loaned to library members. This data mining processing is done using Weka software. The results of processing from the Association Rule data mining obtained 5 itemset combinations with confidence values ​​of 0.97, 0.96, 0.95. With the results of this data mining, the librarian obtains recommendations for grouping and placing books in the library through knowledge of the types of books that are most often borrowed.

Feature Extraction of National Physical Fitness Data Based on Data Mining

In order to better understand and optimize the national physical fitness, this paper puts forward the national physical fitness data change feature extraction method based on data mining, uses the decision tree and association rule data mining algorithm to collect the national physical fitness data in recent years, constructs the database to realize the effective data management, and uses the data mining algorithm to construct the physical fitness change feature evaluation index. Finally, through experiments, it is confirmed that the national physique data change feature extraction method based on data mining has high effectiveness in the process of practical application. It can better understand the national physique change trend and put forward targeted suggestions for national physique health optimization.

Mining groups of factors influencing bus/minibus crash severities on poor pavement condition roads considering different lighting status

Objective This study employs a data mining approach to discover hidden groups of crash-risk factors leading to each bus/minibus crash severity level on pothole-ridden/poor roads categorized under different lighting conditions namely daylight, night with streetlights turned on, and night with streetlights turned off/no streetlights. Methods The bus/minibus data employed contained 2,832 crashes observed on poor roads between 2011 and 2015, with variables such as the weather, driver, vehicle, roadway, and temporal characteristics. The data was grouped into three based on lighting condition, and the association rule data mining approach was applied. Results Overall, most rules pointing to fatal crashes included the hit-pedestrian variable, and these crashes were more frequent on straight/flat roads at night. While median presence was highly associated with severe bus/minibus crashes on dark-and-unlighted roads, median absence was correlated with severe crashes on dark-but-lighted roads. On-street parking was identified as a leading contributor to property-damage-only crashes in daylight conditions. Conclusions The study proposed relevant countermeasures to provide practical guidance to safety engineers regarding the mitigation of bus/minibus crashes in Ghana.

Implementasi Data Mining dengan Metode Apriori Dalam Menentukan Pola Pemilihan Pemeriksaan Kimia

Businesses and organizations that want to survive need to develop effective business strategies. Reviewing the company's transaction data gradually leads to sales data. So it is very if not re-analyzed. The examination products offered at Prodia Laboratories vary, and the exploration of the packages offered influences the public to choose these packages. The Apriori method is a method that can help companies find out which test inspections are the most sold and know the relationship between inspections with one another, and with the help of the RapidMiner software the types of tests or tests that come out at the same time can be known. The a priori algorithm is a type of association rule in data mining (data mining). One of the stages of Association Analysis that has attracted the attention of many researchers to produce efficient algorithms is high-frequency pattern mining. The importance or not of an association rule can be understood with two benchmarks, namely the support value and the confidence value. Support (support value) is the proportion of the combination of these items in the database, while confidence (value) is the strength of the relationship between items in the association rules. From the results of calculations using the Apriori method to determine purchasing patterns on the type of chemical/fat check by determining a minimum support of 35% and 75% confidence, resulting in 3 association rules, namely first, if you buy Total Cholesterol, you will buy LDL Cholesterol with 100% support and 100% confidence, secondly. If Total Cholesterol, then I will buy Triglycerides with 100% support and 100% confidence, buy all three. If I buy LDL Cholesterol, I will buy Triglycerides with 100% support and 100% confidence

Geospatial Distribution of the Quality of the Coffee of Minas Gerais by Methods of Data Mining Techniques

The state of Minas Gerais is located in one of the most privileged Brazilian regions for the production of high-quality coffees. Due to its large territorial extension and environmental variation, it favors the production of specialty coffees with a great diversity of flavor and aroma. These differences are related to the peculiar characteristics of each microregion, particulary climate variations, altitude, latitude and production systems. Although the relationship between environmental characteristics and coffee productivity in different biomes is already well explored, its relationship with beverage quality still requires research. Given the need for more information on areas with potential for special coffee production, this paper proposes to associate data concerning to sensorial quality of coffees in the Cafés of Minas Quality Contest, with climatic and geographical variations of each state region, applying association rules for data mining, used to understand the aptitude of the state through the generation of rules that describe the most relevant patterns in the data. The climatic, geographical and classificatory information of the coffee samples is the result of the database of the contest participants in 2007 and 2008. The results demonstrate that with data mining techniques it was possible to associate the regions with the greatest potential with the climatic and geographical characteristics of the environment.

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

Protecting the privacy of healthcare information is an important part of encouraging data custodians to give accurate records so that mining may proceed with confidence. The application of association rule mining in healthcare data has been widespread to this point in time. Most applications focus on positive association rules, ignoring the negative consequences of particular diagnostic techniques. When it comes to bridging divergent diseases and drugs, negative association rules may give more helpful information than positive ones. This is especially true when it comes to physicians and social organizations (e.g., a certain symptom will not arise when certain symptoms exist). Data mining in healthcare must be done in a way that protects the identity of patients, especially when dealing with sensitive information. However, revealing this information puts it at risk of attack. Healthcare data privacy protection has lately been addressed by technologies that disrupt data (data sanitization) and reconstruct aggregate distributions in the interest of doing research in data mining. In this study, metaheuristic-based data sanitization for healthcare data mining is investigated in order to keep patient privacy protected. It is hoped that by using the Tabu-genetic algorithm as an optimization tool, the suggested technique chooses item sets to be sanitized (modified) from transactions that satisfy sensitive negative criteria with the goal of minimizing changes to the original database. Experiments with benchmark healthcare datasets show that the suggested privacy preserving data mining (PPDM) method outperforms existing algorithms in terms of Hiding Failure (HF), Artificial Rule Generation (AR), and Lost Rules (LR).

Comprehensive Evaluation of Government Economic Management Performance Based on Multidimensional Data Mining in Fuzzy Comprehensive Environment.

Economic, political, social, and military activities all fall under the umbrella of government duties. The modification and reinterpretation of economic functions are the primary focus of the innovation in government administration style under the wave of economic globalisation. The effectiveness of the government's economic administration determines the general level of public administration at the federal level. An essential issue of national public administration that has a significant impact on the political growth of many nations is the performance evaluation of government economic administration. The people are the subject of government economic administration performance evaluation, and the people adjudicate the outcomes. An essential method of assessing the productivity of various departments is through performance review. The nation is currently in the process of switching from its long-standing planned economic system to a market economic system. The internal and external environments of governmental organisations are continually changing in addition to the strong trend of economic globalisation. Determining and advancing the national government's economic administration model is so crucial. The article offers a multidimensional data mining-based optimum design scheme for the thorough assessment of government economic administration performance. The fuzzy comprehensive evaluation rule is an effective method for quantifying the qualitative indicators when the quantitative indicators in the evaluation index system are difficult to measure. It is relatively simple, reasonable, and simple to operate in practise, which is conducive to the thorough and scientific performance evaluation of the government economic administration's science and technology administration functions. Following an assessment of the system's performance using association rule data mining technologies, a simulation test analysis is completed. The accuracy of the proposed arithmetic, which is 8.26% higher than the conventional arithmetic, is demonstrated by simulation results. The development of an evaluation model that incorporates both subjective and objective criteria, as well as the thorough assessment of the effectiveness of government economic administration based on data mining technologies, has excellent application prospects and practical value.

Diagnosis of weak points in the distribution network based on association rules

In the intelligent distribution network, various faults of the distribution network still frequently occur during the operation of the distribution network, which normally appears at the weak points of various types of electrical equipment. The basic concepts and practical application significance of the three judgment indicators of support, confidence, and imbalance ratio in association rules are proposed in this paper; subsequently, the collection and sorting of fault data in the distribution network are mentioned, and the subsequent fault data of the association rules are performed as well. The preprocessing was implemented, and the weak point analysis model of the distribution network was established. Finally, the analysis and verification were carried out through the calculation example, which proved the feasibility and accuracy of the association rule data mining algorithm FP-network in the analysis and diagnosis of the weak point of the distribution network. The results show that the accuracy of the proposed method in identifying weak points in the distribution network can reach more than 90%. Therefore, the application of this algorithm in the monitoring of weak points in the distribution network has better performance.

DATA MINING PEMBENTUKAN POLA PENGGUNAAN ALAT KONTRASEPSI DENGAN METODE ASSOCIATION RULE

Continuous use of contraceptives is a factor influencing the success of the family planning program. As is well known, contraceptives are not necessarily suitable for everyone's condition, therefore women must be able to determine what contraception is suitable for themselves. The problem is that many women find it difficult to decide what contraceptive method to use, apart from a lack of knowledge and information, there is no concept / pattern for choosing a contraceptive device. The purpose of this study is to find patterns of contraceptive use using the Data Mining Association Rule method. The results of the performance of the Apriori Algorithm produce a combination pattern that describes the set of frequent item sets with the highest confidence value, namely 90% In the Rule, if the injectable contraceptive device is 3 months, then the mother's age is 17-35 years. The pattern that is formed is the result of concept formulation, so that this pattern can be used as a reference for prospective acceptors in determining the choice of contraceptives suitable for use.

COVID-19 patient diagnosis and treatment data mining algorithm based on association rules.

Association rules are used in different data mining applications, including Web mining, intrusion detection, and bioinformatics. This study mainly discusses the COVID‐19 patient diagnosis and treatment data mining algorithm based on association rules. General data The key time interval during the main diagnosis and treatment process (including onset to dyspnea, first diagnosis, admission, mechanical ventilation, death, and the time from first diagnosis to admission, etc.), the cause of death by laboratory examination, and so forth. The frequency of drug use was counted and association rule algorithm was used to analyse and study the effect of drug treatment. The results could provide reference for rational drug use in COVID‐19 patients. In this study, in order to improve the efficiency of data mining in data processing, it is necessary to pre‐process these data. Secondly, in the application of this data mining, the main objective is to extract association rules of COVID‐19 complications. So its properties for mining should be various diseases. Therefore, it is necessary to classify individual disease types. During the construction of association rules database, the data in the data warehouse is analysed online and the association rules data mining is analysed. The results are stored in the knowledge base for decision support. For example, the prediction results of the decision tree can be displayed at this level. After the construction of the mining model, the display interface can be mined, and the decision‐maker can input the corresponding attribute value and then predict it. 0.76% of people had both COVID‐19, CHD and hypertension, while 46.5% of people with COVID‐19 and CHD were likely to have hypertension. This study is helpful to analyse the imaging factors of COVID‐19 disease.

Association Rule Mining to Examine Predictors for the Outcome of Gait Rehabilitation Programs in Stroke Survivors.

This study presents a novel application of association rule data mining to determine the predictors of the response to locomotor training and home exercise for improving gait after stroke. The study was a secondary data analysis on the Locomotor Experience Applied Post Stroke Trial dataset. The association rule analysis was applied to analyze three interventions: (1) early locomotor training, (2) late locomotor training, and (3) home exercise program. The outcome variable was whether participants poststroke had greater than median improvement in the self-selected comfortable gait speed. Three types of predictors were investigated: (1) demographics, (2) behavioral and medical history, and (3) clinical assessments at baseline. Association rules were generated when they meet two criteria determined based on the data: 10% of support and 70% of confidence. The identified rules showed that the predictors of the response were different across the three interventions, which was inconsistent with the previous report based on traditional logistic regression. However, the rules were identified with high confidence but low support, indicating that they were reliable but did not appear often in the Locomotor Experience Applied Post Stroke Trial dataset. Further investigation of these rules with a larger sample size is warranted before applying them to clinical settings.

Analysis of frequent itemset generation based on trie data structure in Apriori algorithm

Apriori is one technique of data mining association rules that aims to extract correlations between sets of items in the transaction database. The main problem with the Apriori algorithm is the process of scanning databases repeatedly to generate itemset candidates. This research examines the combination of pruning by using the trieapproach and multi-thread implementation in three algorithms to obtain frequent itemset. Trie is a data structure in the form of an ordered tree to store a set of strings where every node in the tree contains the same prefix. The use of a full combination trie (different from frequent pattern (FP) tree using links) allows the implementation of arrays and the hash calculation to achieve the addressing of itemset combination. In this research, the measure to get the address is called Hash-node calculation used to update support value. For these three alternatives, run time processing is analyzed based on the number of itemset combinations and transaction data at a certain minimum support value. The experimental results show that an algorithm thatexploits resource capabilities by applying multi-threadperforms almost seven times betterthanan algorithm implemented in single-thread in calculating hash-node. The fastest run time of the multi-thread approach is 43 minutes with 150-itemset combinations on 100,000 transaction data.