Association Rule Mining Algorithm Research Articles

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from “Rest” injuries, affirming the NBA’s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring.

Customer Feedback Analysis on Smart phones Using Apriori Algorithm

Smartphones play a dynamic role in our daily lives, enriching with the tools of Knowledge Data Discovery (KDD) and data mining as a mobile device. This assists in acquiring and deriving better contextual data about users who are utilizing their mobile phones. Data mining is used to search and analyze the data to recognize the potential information. In contrast, data mining has several computational patterns, models, and algorithms to analyze the customer feedback of the smartphone and its features. The Apriori algorithm is one of the standard algorithms for Association Rule Mining (ARM) that can be used to mine frequent item sets and their associate rules.To classify sentiments into positive and negative polarity, the supervised ML is applied. By training data sets to train a model, and then testing data sets to validate the model works as expected. The fields of ML have several challenges namely text mining, associative mining,image processing or classification problems, and, sentiment classification which endure to resolve in this area. Significant experiments utilizing the methods on PAUL, PID, and GitHub datasets demonstrate that the methods significantly evaluated utilizing the Product Identification data set are involved for the analysis of the precision, recall, f-measure as well as accuracy metrics. This research focuses on improving the efficiency of the various mobile brand patterns using the customer Review process method to discover a suitable mobile review analysis.

Research on the Path Construction and Practice of Bidirectional Cultural Mutual Guidance in Higher Vocational English Course Civics Teaching under the Background of Big Data

Abstract This study delves into the effective integration of bi-directional cultural intercultural instruction with Civic and Political teaching in a higher vocational English course and analyzes it with an association rule mining algorithm. By screening the weighted candidate itemsets through the weighted support degree model, the study identifies the itemsets’ positive and negative correlation attributes and mines the association rules between English teaching and students’ bidirectional cultural interduction ability by combining with the decision support model. The article also analyzes the content of Civics and Chinese and foreign cultural elements in the English curriculum. It compares and examines the changes in students’ two-way cultural core literacy and its introduction effect. The results of the study show that after the bi-directional cultural Civics teaching in the English curriculum, students experienced significant changes in worldview, values and Chinese and foreign cultural cognition, with t-values of −4.312, −5.034, and −4.275, respectively, and p-values of less than 0.001. In addition, the students’ bi-directional cultural communication competence improved significantly, with scores ranging from 3.8 to 4.3. The study provides valuable insights into the promotion of Chinese-foreign cultural communication and the reform of higher vocational English teaching.

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China.

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

A hybrid machine learning model combining association rule mining and classification algorithms to predict differentiated thyroid cancer recurrence.

Differentiated thyroid cancer (DTC) is the most prevalent endocrine malignancy with a recurrence rate of about 20%, necessitating better predictive methods for patient management. This study aims to create a relational classification model to predict DTC recurrence by integrating clinical, pathological, and follow-up data. The balanced dataset comprises 550 DTC samples collected over 15 years, featuring 13 clinicopathological variables. To address the class imbalance in recurrence status, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) was utilized. A hybrid model combining classification algorithms with association rule mining was developed. Two relational classification approaches, regularized class association rules (RCAR) and classification based on association rules (CBAR), were implemented. Binomial logistic regression analyzed independent predictors of recurrence. Model performance was assessed through accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. The RCAR model demonstrated superior performance over the CBAR model, achieving accuracy, sensitivity, and F1 score of 96.7%, 93.1%, and 96.7%, respectively. Association rules highlighted that papillary pathology with an incomplete response strongly predicted recurrence. The combination of incomplete response and lymphadenopathy was also a significant predictor. Conversely, the absence of adenopathy and complete response to treatment were linked to freedom from recurrence. Incomplete structural response was identified as a critical predictor of recurrence risk, even with other low-recurrence conditions. This study introduces a robust and interpretable predictive model that enhances personalized medicine in thyroid cancer care. The model effectively identifies high-risk individuals, allowing for tailored follow-up strategies that could improve patient outcomes and optimize resource allocation in DTC management.

Research on Physical Training Helping College Students’ Physical Fitness and Health Quality Development in the Context of “Healthy China”

Abstract In the face of the current dilemma and predicament of students’ physical health, it has become inevitable to incorporate health education into the national education system, with the help of physical training to reshape cognition, establish beliefs, guide behavior, and other effective means, so as to achieve the fundamental purpose of improving the physical health of students. This paper takes college students’ physical health as the research object, carries out the research on efficient association rule mining algorithm for BMI index, uses Apriori association rule algorithm in R language data mining to carry out in-depth mining of physical health test data, and carries out in-depth comparative analysis and exploration of intrinsic information of physical test data of two academic years of 2022 grade in a school in a more detailed and in-depth way. A scientific guidance program for physical training is created using the collaborative filtering algorithm to support the high-quality development of college students’ physical fitness and health. The results of the study found that the correlation coefficient between physical training behavior and BMI index r = -0.753, confidence test value P = 0.0004 < 0.05, indicating that there is a significant negative correlation between students’ physical training behavior and BMI. That is, physical education and sports can significantly promote the physical health of college students, and ultimately, their BMI should fall within the normal range.

Analysis of Strategies and Effectiveness of Big Data Technology for Business Decision Optimization

Abstract Business decision-making is a decision-making process that relies heavily on business data, and with the continuous improvement of information technology, the level of intelligence of business decision-making has been increasing. The paper suggests relevant optimization strategies for business decision-making and creates an intelligent business decision-making process that utilizes big data. This paper mainly applies the association rule mining algorithm based on big data to business decision-making, extracting valuable information for business decision-makers from a large database of business information systems. In order to verify the operational efficiency of the improved algorithm, the traditional Apriori algorithm, FP-Growth algorithm, HBE-Apriori algorithm, and the improved algorithm are compared at the same time, and the results of the experiments show that the improved algorithm’s operational efficiency in the process of finding the maximal frequent itemset is significantly improved compared with the other three algorithms, which provides a basis for the analysis of the effectiveness of the optimization of business decision-making. The example of optimization of business decisions through big data technology found that after the optimization of business decision optimization, the asset quality and solvency of the enterprise are improved, and the operating capacity is unchanged due to the low operating capacity of the total assets of the enterprise. This paper’s method for optimizing business decisions based on association rules is shown to be feasible.

A statistical study on the similarities and differences of sports interests among college students

Abstract This study focuses on the status quo of group exercise interest differences and similarities among college students, and proposes the interest scalar law of group exercise for the group exercise data of college students, which measures the frequency of group exercise based on the parameter and activity. Based on the interest degree indexes of exercise duration and time, the interest degree model of group exercise is constructed to explore the laws of group exercise behavior. An association rule mining algorithm based on genetic algorithms and interest degrees is proposed, and an improved fitness function is proposed to optimize the algorithm. Taking China’s province S as an example, the statistics on group sports interests of college students show that the central tendency among group sports interests is ball group sports. Among the influencing factors of interest dissimilarity, the gender influencing factor significantly differs in the dimensions of psychological state and peer influence (p<0.05). There were significant differences (p<0.05) and highly significant differences (p<0.01) in the p-values of the place of origin influence factor in the course offerings and psychological state dimensions, respectively. The group sport type influence factor, on the other hand, showed significant differences (p<0.01) in all four sizes, including psychological state and family status.

Scheduling Constraints and Implementation in Teaching Management System Incorporating Association Rule Mining Algorithm

Scheduling Constraints and Implementation in Teaching Management System Incorporating Association Rule Mining Algorithm

ADSA – Association-Driven Safety Analysis to Expose Unknown Safety Issues

Autonomous systems are susceptible to unknown safety issues due to overlooked dependencies among components of the system and the entities that are part of its operating environment. The current safety analysis techniques aids in identifying known safety issues but not overlooked/unknown safety issues. To identify unknown safety issues due to problematic interactions between components, in our previous work, we proposed safety assessment for concurrent components (SACC). Despite being more effective than FMEA and goal modeling, SACC suffers from some limitations such as not considering environmental entities and their properties, and a manual process for identifying associated components for the collective analysis. For a complex system with a large number of components, such an analysis can result in overlooking safety issues. To address these limitations, in this paper, we propose an association-driven safety analysis (ADSA) approach, which is extended and built on SACC. The approach uses a property-relation (PR) table and modified association rule mining algorithm to identify components and environmental entities that need to be considered together to detect overlooked or unknown safety issues. We evaluated our approach using four robotic systems and compared with SACC and systems theoretic process analysis (STPA). Our results show that our proposed approach, in particular using behavioral dependencies, is effective at exposing unknown safety issues.

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Abstract This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

An Algorithm for Data Management of Higher Education Based on Fuzzy Set Theory - Association Rule Mining Algorithm

An Algorithm for Data Management of Higher Education Based on Fuzzy Set Theory - Association Rule Mining Algorithm

A novel software defect prediction approach via weighted classification based on association rule mining

Software defect prediction technology is used to assist software practitioners in effectively allocating test resources and identifying hidden defects in a timely manner. However, the prediction of defect-prone software using association rule mining algorithms is limited because of the unbalanced distribution of defect data. Furthermore, although the existing weighted association rule mining approach considers item strength, the weight calculation still relies on expert experience and lacks fine granularity. We propose a novel software defect prediction approach based on mutual information and correlation coefficient weighted class association rule mining (MCWCAR). The MCWCAR model employs a cost-sensitive strategy and generates frequent itemsets according to three mining objectives while maintaining the original item distribution: defective class rules, non-defective class rules, and feature association relationships. During the weighted frequent itemset mining process, it combines feature selection and itemset screening to determine the appropriate feature combination through mutual information weighted support. Meanwhile, the correlation coefficient is applied to accurately depict the correlation between feature items and defect classes, serving as the weight to mine class association rules. Additionally, to ensure that interestingness measures have asymmetry and effectively represent negative associations under the condition of class imbalance, the addedvalue is adopted in the filtering association rules. We conducted experiments on 27 open-source datasets and evaluated the performance differences between MCWCAR and state-of-the-art baseline classifiers. Experimental results demonstrate that the proposed algorithm significantly outperforms other baselines in terms of Balance, Gmean, MCC, and F-measure.

Calibrating subjective data biases and model predictive uncertainties in machine learning-based thermal perception predictions

Heating, Ventilation, and Air Conditioning (HVAC) systems in large-scale buildings often struggle to ensure satisfactory thermal comfort for diverse occupants while minimizing energy waste. Achieving this goal requires developing reliable prediction models that capture the changing and varied occupant thermal perceptions in different spaces. Despite their widespread use, many machine learning (ML) based prediction models suffer from subjective data biases and model predictive uncertainties, causing inaccurate estimation for occupant needs and leading to suboptimal building controls. The authors propose a data-model integration method that identifies and calibrates the inherent uncertainties of existing ML models in both data and model dimensions, ensuring reliable thermal perception predictions. This method introduces the Multidimensional Association Rule Mining (M-ARM) algorithm to identify biased human responses by exploring interrelationships among four perception metrics: thermal sensation, comfort, acceptability, and preference. Our method reveals significant performance enhancements in seven ML models, enhancing the F1-score by up to 5.53%. By leveraging reliability diagrams and Expected Calibration Error (ECE) scores, we also expose the models’ vulnerability to miscalibration and the need for calibrated predictions. We further evaluate six calibration techniques (e.g., Platt Scaling and Isotonic Calibration) on these models and uncover their potential to enhance prediction reliability performance, highlighting a reduction of up to 80.66% in ECE scores. The authors also investigated the impacts of dataset sizes, classifiers, and calibration methods on the proposed method. Our research offers insight into creating robust data-driven strategies for thermal perception predictions, ultimately contributing to optimized occupant comfort and energy efficiency in buildings.

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

AbstractNumerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future.

Digitalization of university education management applicable to association rule mining algorithms

Abstract In this paper, firstly, based on the digital construction of education management in colleges and universities, it is proposed to construct an education management system for colleges and universities to realize the digital development of education management and to design the function and database of the education management system of colleges and universities. Secondly, the Apriori algorithm in association rule mining algorithm is used to define the minimum support and minimum confidence in the variable parameter focus, then the support threshold and confidence threshold are set in the process of optimization connection and pruning, and for the shortcomings of traditional algorithms, the relevance metrics are cited here to improve the systematic data mining efficiency of the Apriori algorithm. Then, the simulation experiment environment and data set are selected, the evaluation index system is determined according to the system requirements, and the education management system based on the association rule mining algorithm is simulated and analyzed. The results show that when min_supp=70%, the Apriori algorithm generates a total of 60 frequent itemsets, and the running time is about 21.2s. Overall, the Apriori algorithm only reads the data set twice, so the program running time is shorter than the Eclat algorithm running time.

Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm.

Thyroid Cancer (TC) is the most frequent endocrine malignancy neoplasm. It is the sixth cause of cancer in women worldwide. The treatment process could be expedited by identifying the controlling molecular mechanisms at the early and late stages, which can contribute to the acceleration of treatment schemes and the improvement of patient survival outcomes. In this work, we study the significant mRNAs through Machine Learning Algorithms in both the early and late stages of Papillary Thyroid Cancer (PTC). During the course of our study, we investigated various methods and techniques to obtain suitable results. The sequence of procedures we followed included organizing data, using nested cross-validation, data cleaning, and normalization at the initial stage. Next, to apply feature selection, a t-test and binary Non-Dominated Sorting Genetic Algorithm II (NSGAII) were chosen to be employed. Later on, during the analysis stage, the discriminative power of the selected features was evaluated using machine learning and deep learning algorithms. Finally, we considered the selected features and utilized Association Rule Mining algorithm to identify the most important ones for improving the decoding of dominant molecular mechanisms in PTC through its early and late stages. The SVM classifier was able to distinguish between early and late-stage categories with an accuracy of 83.5% and an AUC of 0.78 based on the identified mRNAs. The most significant genes associated with the early and late stages of PTC were identified as (e.g., ZNF518B, DTD2, CCAR1) and (e.g., lnc-DNAJB6-7:7, RP11-484D2.3, MSL3P1), respectively. Current study reveals a clear picture of the potential candidate genes that could play a major role not only in the early stage, but also throughout the late one. Hence, the findings could be of help to identify therapeutic targets for more effective PTC drug developments.

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

Association rules mining with the Chinese social insurance fund dataset can effectively discover different kinds of errors, irregularities, and illegal acts by providing auditors with relationships among the items and therefore improve auditing quality and efficiency. However, traditional positive and negative association rules (PNARs) mining algorithms inevitably produce too many meaningless or contradictory rules when these two types of rules are mined simultaneously, which brings a huge challenge to auditors retrieving decision information. Aimed to reduce the quantity of low-reliability PNARs without missing interesting rules, this paper first proposes an improved PNARs mining algorithm with minimum correlation and triple confidence threshold to control the mined rules number by narrowing the range of confidence settings. Then, a novel pruning algorithm based on the inclusion relation of the rule’s antecedent and consequent is given to remove those redundant rules. After that, the proposed optimized PNARs mining approach is applied to the Chinese social insurance fund dataset starting with audit features influence factors mining using the Hash table. The experimental results with different datasets show that the proposed framework not only can ensure effective and interesting rules extraction but also has better performance than traditional approaches in both accuracy and efficiency, reducing the number of redundant PNARs by over 70.1% with experimental datasets and average 78.5% with auditing data mining, respectively.

A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors

Under the background of large-scale PV (photovoltaic) integration, generating typical operation scenarios of power systems is of great significance for studying system planning operation and electricity markets. Since the uncertainty of PV output and system load is driven by weather factors to some extent, using PV output, system load, and weather data can allow constructing scenarios more accurately. In this study, we used a TimeGAN (time-series generative adversarial network) based on LSTM (long short-term memory) to generate PV output, system load, and weather data. After classifying the generated data using the k-means algorithm, we associated PV output scenarios and load scenarios using the FP-growth algorithm (an association rule mining algorithm), which effectively generated typical scenarios with weather correlations. In this case study, it can be seen that TimeGAN, unlike other GANs, could capture the temporal features of time-series data and performed better than the other examined GANs. The finally generated typical scenario sets also showed interpretable weather correlations.

An Improved Apriori Algorithm for Association Rule Mining in Employability Analysis

An Improved Apriori Algorithm for Association Rule Mining in Employability Analysis