Association Rule Mining Algorithm Research Articles

Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach

The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

Predicting crash injury severity in road freight flows with association rules algorithms

The purpose of this study is to evaluate the use of the Apriori association rule mining algorithm to classify and predict the severity of the 718,565 accidents involving freight transport vehicles in Mexico, which occurred between 2009 and 2018. The accidents were classified into those in which there was only material damage or injured people {Severity=0} and in those in which people died {Severity=1}. 115 association rules were obtained, 79 corresponding to non-fatal accidents, and 36 to fatal ones. The main factors associated with the severity of the accident belong to male subjects, involved in accidents that occur on weekends and in suburban areas, and where the probability of the accident being fatal is 1.69 times greater. Thus, the results of using the association rules to relate demographic and circumstantial characteristics of the accident with the severity of the injuries show an accuracy of just over 65%. Therefore, despite the limitations that may occur due to the omission of relevant variables, and the fact that the results show little precision, the feasibility of using machine learning techniques and, specifically, the association rules as promising tools to help analyze accidents and help launch road safety interventions more effectively is manifested.

A hybrid association analysis framework of accident reports for offshore drilling platforms

As the key work unit of offshore oil and gas exploration and exploitation activities, an offshore drilling platform (ODP) integrates high-density equipment and facilities in a relatively narrow area, making it prone to frequent serious accidents under the influence of environmental factors. The mining association rules of the hidden dangers from the accumulated historical accident reports can help find the potential causes of the risks and proactively reduce the risk of accidents and damages. However, due to the professionalism of the corpus linguistics of the offshore oil and gas industry and the complex correlations between multiple named entities, mining valuable information from massive transaction datasets has increasingly become a challenge for academia and industry. To this end, a hybrid association analysis framework for ODPs is proposed in this paper, which is divided into four stages: data preprocessing, information extraction, association rule mining and making a rectification plan. In the key information extraction stage, we propose a three-layer deep learning network based on the BERT-IDCNN-BiLSTM-CRF (BIBC) model, which makes effective use of the semantic information of the context to extract the trituples of the hidden dangers (i.e., equipment, location and phenomenon). Based on the extracted entities, an effective multientity association rule mining algorithm based on hash technology and an inverted itemset (HTApriori) as a variant of Apriori for ODP databases is developed to understand the many-to-one, one-to-many and many-to-many risky causes between the entities. A series of real-world experimental results show that the proposed framework offers more reliable and effective performances of information extraction and association rule mining than the comparison methods. By analyzing the association results, the hidden dangers and their association rules, which occur with some small probability but are critical in ODPs, can be found. This work is helpful in significantly improving the rationality of the rectification plans. In addition, the application results on the CoNLL2003 dataset further prove the generalization performance of the proposed method.

SufRec, an algorithm for mining association rules: Recursivity and task parallelism

The mining of frequent itemsets and association rules is a core problem in data mining and an essential task in data analysis. In this paper, we present SufRec, a new algorithm for finding frequent itemsets and association rules. We give two versions. In both versions, the mining of the frequent itemsets is decomposed as a sequence of tasks. The first version (the SufRecDep algorithm) proceeds the tasks successively, each one using the results of the previous ones. The second version (the SufRecInd algorithm) performs the tasks independently of each other. Both versions are recursive with respect to the items and have thus the advantage to be particularly efficient for updating the mining process when new items are added to the database or when others are excluded. Moreover, the task-independent processing of SufRecInd makes it very easy to build parallel versions of SufRec. We present two parallel SufRec algorithms. With the first one, P-FIFO-SufRec, the processors perform the tasks according to the order in which the items appear in the database, preserving the recursive nature of SufRec. For second one, P-PAST-SufRec, the set of items is fixed and the tasks are pre-assigned to each processor. In order to evaluate the performance of SufRec, we carry out a consequent experimental study. In particular, we compare its running times with those of well-known parallel and non-parallel frequent itemsets mining algorithms.

Retracted: Association Rule Mining Algorithm in College Students’ Quality Evaluation System

Retracted: Association Rule Mining Algorithm in College Students’ Quality Evaluation System

Current situation and training strategies of preschool students’ skills in the Internet era

Abstract The analysis of the current situation of skills and training strategies of preschool students in colleges and universities is to provide more professional talents for the cause of early childhood education. This paper first gives a brief description of the association rule mining algorithm under Internet technology, gives the evaluation method of association rules, including support, confidence, and enhancement, and gives the basic process of association rule mining. Next, it is explained that the multi-objective optimization problem utilizes the objective function optimization for calculating the optimal solution, and the commonly used multi-objective optimization problem solution and the selection method of the fitness function are given. Then the principle of the whale optimization algorithm is described and explained, the whale optimization algorithm is used to optimize the objective function of association rules, and then the steps and processes of the WOA-ARM algorithm are obtained. Finally, the algorithm was used to mine the training strategy of GZ University as an example, the “situational experience” teaching and training method was proposed, and the teaching characteristics and objectives of this method were analyzed. Regarding teaching objectives, 48.75%, 32.27%, 12.39%, and 6.59% of the ratings were A, B, C, and D, respectively. This indicates that the “situational experience” teaching training is suitable for college preschool students, and there is a strong correlation between the current skills and the training strategy.

Application of Association Rule Mining Algorithm based on 5G Technology in Information Management System

In this paper, an application method of association rule mining algorithm based on 5G technology in information management system is proposed to solve the problems of long running time and low processing efficiency in traditional financial information processing system. The association rule mining algorithm's employment in information management systems is the main topic of this research study, which is based on 5G technology. The efficiency and efficacy of information management systems have a lot of room to grow with the introduction of 5G. Large datasets may be mined for patterns and associations using the potent approach known as association rule mining. We want to improve the performance of information management systems by fusing association rule mining with the capabilities of 5G technology. The experimental findings indicate that in the first group of trials, the traditional system’s time for information mining is identical to that of the developed system, which is around one minute. The typical system's time to mine financial information, however, steadily grows with the amount of experimental data. The difference between the two is most obvious in the sixth experiment. Because the design system can delve deeply into the financial information, the overall information mining time of the financial information management system based on the association rule mining algorithm of the design is shorter. It is confirmed that the system for automatically processing financial information described in this study has a high level of processing accuracy and a positive processing outcome.

Exploring the path of integration of rural tourism culture and food industry in the context of big data

Abstract Under the background of big data, better integrating rural tourism culture and the food culture industry becomes an important research direction to improve the quality of rural tourism and promote rural revitalization. Based on the association rule mining algorithm under big data technology, this paper analyzes the keyword data of tourists for rural tourism culture and food culture on the Internet review platform by combining the survey and research on rural tourism culture resources and food culture resources in Y town. Results: The number of tourists participating in rural tourism was 51.2% for males and 48.8% for females. In terms of age composition, visitors aged 26-60 accounted for 82.5% of the total, thus indicating that middle-aged and older people in pursuit of nostalgia and local flavor dominate rural tourism. Regarding income level, 81.1% of the rural tourism tourists were in the middle and high end. In terms of tourists’ education, 36.1% of the tourists’ education level is undergraduate, while the rest of the education levels are 7.5%/11.2%/19%/26.2%, respectively, indicating that people with high education are more willing and know how to pursue the culture embedded in the countryside. From the big data sentiment analysis of tourists’ evaluation words, the most frequently appearing words are “special” and “delicious”, reaching 492 and 465 times, respectively, while the least frequent keyword is “dangerous”. The least frequent keyword is “dangerous”, but it also appears 61 times. From the viewpoint of experience, the overall experience of rural tourism culture and food culture of tourists is 76.41% and 79.81%. With the above analysis, in the process of integrating the development of rural tourism culture and food culture, we should focus on creating local characteristic brands, developing characteristic food culture, building characteristic rural tribes, and preventing uniformity from enhancing the attractiveness of the countryside and providing a new road for promoting rural revitalization.

Decoding the Possible Molecular Mechanisms in Pediatric Wilms Tumor and Rhabdoid Tumor of the Kidney through Machine Learning Approaches

Objective: Wilms tumor (WT) and Rhabdoid tumor (RT) are pediatric renal tumors and their differentiation is based on histopathological and molecular analysis. The present study aimed to introduce the panels of mRNAs and microRNAs involved in the pathogenesis of these cancers using deep learning algorithms. Methods: Filter, graph, and association rule mining algorithms were applied to the mRNAs/microRNAs data. Results: Candidate miRNAs and mRNAs with high accuracy (AUC: 97%/93% and 94%/97%, respectively) could differentiate the WT and RT classes in training and test data. Let-7a-2 and C19orf24 were identified in the WT, while miR-199b and RP1-3E10.2 were detected in the RT by analysis of Association Rule Mining. Conclusion: The application of the machine learning methods could identify mRNA/miRNA patterns to discriminate WT from RT. The identified miRNAs/mRNAs panels could offer novel insights into the underlying molecular mechanisms that are responsible for the initiation and development of these cancers. They may provide further insight into the pathogenesis, prognosis, diagnosis, and molecular-targeted therapy in pediatric renal tumors.

Unveiling Thyroid Disease Associations: An Exceptionality-Based Data Mining Technique

Background: The prevalence of thyroid disease has seen a rapid increase in recent times, primarily attributed to the fast pace of lifestyles that often result in poor dietary choices, work-life imbalances, social stress, genetic mutations, and improved diagnostic capabilities. However, the precise contribution of these factors to thyroid disease remains a subject of controversy. Consequently, there is a pressing need to gain a comprehensive understanding of the related associations in order to potentially mitigate the associated morbidity and mortality rates. Methods: This study employed association rule mining techniques to reveal hidden correlations among complex and diverse epidemiological connections pertaining to thyroid disease associations. We proposed a framework which incorporates text mining and association rule mining algorithms with exceptionality measurement to simultaneously identify common and exception risk factors correlated with the disease through real-life digital health records. Two distinctive datasets were analyzed through two algorithms, and mutual factors were retained for interpretation. Results: The results confirmed that age, gender, and history of thyroid disease are risk factors positively related to subsequent thyroid cancer. Furthermore, it was observed that the absence of underlying chronic disease conditions, such as diabetes, hypertension, or obesity, are associated with reduced likelihood of being diagnosed with thyroid cancer. Conclusions: Collectively, the proposed framework demonstrates its sound feasibility and should be further recommended for different disease in-depth knowledge discovery.

A novel machine learning‐based classification approach to prevent flow table overflow attack in Software‐Defined Networking

SummarySoftware‐defined networking (SDN) is an emerging networking architecture where the network control is physically separated from the forwarding plane. It is dynamic, manageable, cost‐effective, and flexible, which is ideal for today's high‐bandwidth applications. The flow table is the fundamental data structure residing in ternary content addressable memory (TCAM) that provides flow rules for incoming flows. The TCAM is the costliest part of an SDN switch that can store limited flow rules. Hence, it is susceptible to distributed denial of service (DDoS) and more specifically to Transformed DDoS (TDDoS). It targets the flow tables, exhausting its limited resource, and resulting in flow table overflow. During overflow, the controller is incapable of installing new rules to the switch, and the switch function is disabled from the network. In this paper, a novel framework named machine learning‐based overflow prevention (MLOP) was proposed which includes a victim switch was identified using association rule mining algorithm concepts. Then, the fuzzy C‐means (FCM) was applied to classify the attack in the victim switch. In addition, FCM ensemble with an optimizing method called dynamic time warping (DTW) which detects similar patterns in the time window during classification. Finally, an elbow method is used that determines the cluster count for optimizing clusters which increases the attack detection and also increases the holding time with reduced packet loss. The real‐time network traffic datasets were used for simulations, and the results were compared with other state‐of‐the‐art approaches. The experimental results show that MLOP increases the holding time by 15% on average and reduces the packet loss due to flow table Overflow by 22.81% than the other existing approaches. In addition, the throughput is increased in the proposed MLOP from 10% to 40%, and end‐to‐end delay is reduced between the variations of 10% to 70%.

Application of Association Rule Mining Algorithm in Talent Training Assessment in Universities Under Social Sustainability

From the contradiction between the difficulty of employment in enterprises and the difficulty of employment in college students, we can see that the strategies of cultivating talents in colleges and universities are biased. This research combines the talent cultivation mode of colleges and universities with the current situation of social development, constructs a comprehensive quality assessment system for students, and uses hierarchical analysis and expert opinions to determine the weight coefficients of each index. In response to the shortcomings of traditional mining algorithms, an improved APRIORI algorithm based on the bit-array matrix is proposed to improve the mining efficiency by introducing the duplicate terms of identification and variable records in data compression. The experimental results show that the improved algorithm can achieve fast convergence on different data sets. The minimum running time of BM Apriori algorithm in different nodes is 102s; under different supports, the average running time of BM Apriori algorithm is 1.49s, and the efficiency is improved by 81.4%. BM Apriori algorithm is used for data mining, and the association rules between courses and students’ grades obtained show that students’ grades are prone to decline during the freshman to sophomore years. Schools should focus on cultivating students’ good learning habits, cultivating excellent talents, and promoting the sustainable development of society.

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.

AI Driven Customer Segmentation and Recommendation of Product for Super Mall

The prime objective of the paper is to propose customer segmentation for the retail customer for a large retail chain super mall. Along with customer segmentation, product recommendation is integrated with the system. Segmentation is required to identify the customer with specific behaviour. The real-world data comes from the customer support department of a retail chain super mall. For customer segmentation, the k- means algorithm is applied by using RFM data and the Association Rule Mining algorithm is applied to create the mapping rules between customer segments and favourite products. The customers are segmented into “Traditional”, “High Spending”, “Occasional”, “Low Spending”, “Disloyal” and “Frequent Buyer”. Customer segmentation is necessary for efficient marketing strategy providing discounts, promotions, campaigns, etc. Moreover, the ARM algorithm is used to map the preferred combo for all customer segment. This combined approach is used to create an effective marketing strategy for retail chain management.

Analysis of multiple chronic disease characteristics in middle-aged and elderly South Koreans by exercise habits based on association rules mining algorithm

BackgroundThe term, “multiple chronic diseases” (MCD), describes a patient with two or more chronic conditions simultaneously at the same time. Compared with general chronic diseases, it is linked to poorer health outcomes, more difficult clinical management, and higher medical expenses. Several existing MCD guidelines support a healthy lifestyle including regular physical activities but do not include specific exercise therapy recommendations. This study aimed to understand the prevalence and model of MCD in middle-aged and elderly South Koreans by comparing MCD characteristics with exercise habits, to provide a theoretical basis for the implementation of exercise therapy in these patients.MethodsThe data of 8477 participants aged > 45 years from the “2020 Korean Health Panel Survey” were used to analyze the current status of MCD in the middle-aged and elderly. The Chi-square test for categorical variables and the t-test for continuous variables. the used software was IBM SPSS Statistics 26.0 and IBM SPSS Modeler 18.0.ResultsIn this study, the morbidity rate of MCD was 39.1%. Those with MCD were more likely to be female (p < 0.001), seniors over 65 years of age (p < 0.001), with low education level, no regular exercise behavior (p < 0.01). Chronic renal failure (93.9%), depression (90.4%), and cerebrovascular disease (89.6%) were the top three diseases identified in patients with MCD. A total of 37 association rules were identified for the group of individuals who did not engage in regular exercise. This equated to 61% more than that of the regular exercise group, who showed only 23 association rules. In the extra association rules, cardiovascular diseases (150%), spondylosis (143%), and diabetes (125%) are the three chronic diseases with the highest frequency increase.ConclusionsAssociation rule analysis is effective in studying the relationship between various chronic diseases in patients with MCD. It also effectively helps with the identification of chronic diseases that are more sensitive to regular exercise behaviors. The findings from this study may be used to formulate more appropriate and scientific exercise therapy for patients with MCD.

The application of enterprise financial management information system in the big data environment

In the rapid development of information technology in our country, the financial management informationization of enterprises has become the main direction of modern enterprise reform and development, the purpose is to provide scientific basis for investment management, product research and development, market delivery and so on. Financial management is the core content of enterprise operation and management. In the big data environment, the traditional enterprise financial management information system has been unable to meet the business needs. Therefore, some scholars proposed to use information technology to provide reference basis for scientific decision-making of enterprise financial management. Based on the understanding of enterprise financial management under the era of big data, on the basis of information system application research status quo, mainly discusses the association rule mining algorithm as the core of financial management information system structure, and combining the practice case to verify the effectiveness of the new enterprise financial management information system application value, consistent with the time development of the management measures are put forward, In order to adapt to the continuously changing market environment faster.

WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges

The cluster effect of specialty groups plays an important role in the development of Higher Vocational Colleges. The purpose of this research is to scientifically explore the interaction mech- anism of specialty groups clustering indexes in higher vocational colleges, uantitatively analyze the correlation of these indexes, nd explore reasonable measures to promote the specialty groups clustering effect in higher ocational olleges. Firstly, data denoising and field screening were car- ried out on the original data, and then the variables were clustered and divided into LHS (Left Hand Side) and RHS (Right Hand Side). Then, an improved multi-dimensional interactive Apri- ori association rule mining algorithm considering index weights and orientation constraints was proposed. The improved Apriori algorithm and the traditional Apriori algorithm were applied to mine the structured data sets. The results show that the improved WOMDI-Apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional Apriori algorithm. The results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; The majr category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart mployment rate; for association rules where the uccessor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. For association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. The framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges.

CANCER PREDICTION IN INFLAMMATORY BOWEL DISEASE PATIENTS BY USING MACHINE LEARNING ALGORITHMS

Colon cancer is one of the most common spread cancers in the world, which leads to total death of 10%. Prediction of onset of cancer, and the cause of its development in these patients can be of an enormous help and relief to those affected, as they can get back their “normal” life. Data mining and machine learning are important intelligent tools for classification, prediction and hidden relation extraction between patient information. We collected data from Shahid Faghihi Hospital in Shiraz. Features collected are as follows: Gender, age, duration of cancer before surgery, number of times the patients used bathroom, taking anti-inflammatory drug prednisolone, duration of drug use and dosage, kind of surgery and number of times consulted and retreatment of surgery, incontinence, etc. After pre-processing and data cleaning stages, effective features were extracted, and also occurrence of cancer predicts by using different classification algorithms. Then association rule mining algorithms like Apriori were used for obtaining any internal hidden relation between entries. Approaching them with different algorithms and assessing them with support vector machine was with highest prediction accuracy (84%). Due to unbalanced dataset, we chose cost sensitive support vector machine. In another aspect, after applying Apriori algorithm, the conditions of non-inflammation were extracted based on dataset features. Some significant outcomes are in what follows. If surgery treatment or diagnosed was less than 5 years, the possibility of developing colon cancer is lower. Also, as the duration of disease increases, the possibility of reoperation increases, as confirmed by the interiors. Since this issue with these features was raised for the first time in this paper at the suggestion of internists, early detection of cancer and also the extraction of effective laws can be of help to the medical community. In future, to get higher accuracy, the improvement of the dataset in terms of number of samples and colonoscopy image features is considered.

New Spark solutions for distributed frequent itemset and association rule mining algorithms

AbstractThe large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations.

Association rule mining for prediction of COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. The catastrophic shock of COVID-19 in India is also enormous. Currently, India has the largest number of COVID cases in Asia. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Based on a large clinical dataset, a linear regression model is also proposed having an accuracy of 73.9% in correctly predicting the occurrence of COVID-19.