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This paper summarizes 13 indoor space design indicators that affect perception and behavior from three aspects: physical elements, other elements, and spatial attributes. Then, through the K-means algorithm and FP-growth correlation rule algorithm, we obtain the user’s behavioral habit data and carry out the simple correlation analysis and curve estimation regression analysis of the user’s perception and behavior based on the indexes in turn. On this basis, it is proposed that the indoor space stay time factor of green art users and spatial perception have 10 highly significant correlation indexes with 0.01 boundaries (two-tailed). Under the quadratic function model of the time factor R =0.3425, the width in the spatial scale and the significance of the time factor have a P-value of 0.0065. The peak value of indoor natural light intake of the neighborhoods adopting the green design is 2,667.3% higher than that of traditional neighborhoods on cloudy days. The cost is 2667.3951 more, but the green interior design that is based on perception and behavioral association is highly effective.

The extraction of association rules remains a crucial strategy in data analysis, particularly in the context of massive datasets. This method unveils complex relationships, correlations, and meaningful patterns within vast datasets, providing essential insights for decision-making and understanding behaviors. Our approach stands out through the use of clustering algorithms for intelligent data partitioning. This strategic choice establishes a robust foundation for efficient association rule extraction. By organizing data specifically through clustering techniques before applying the extraction algorithm, we aim to optimize the relevance and significance of the discovered rules.

Technological innovation is vital for the survival and development of enterprises. In the era of intelligent information interconnection and knowledge-driven economy, there is a growing interest in how to manage high-volume data, unlock its potential value, and provide intelligent analysis and decision-making support for enterprise’s technological innovation. This paper proposes an improved knowledge association analysis method based on the semantic concept model. This approach enables the discovery of potential correlations and interaction modes between the influencing factors of enterprise’s technological innovation, and provides a useful reference for decision-making by combining the analysis with the enterprise’s own situation.

The present work is a part of the ESTenLigne project which is the result of several years of experience for developing e-learning in Sidi Mohamed Ben Abdellah University through the implementation of open, online and adaptive learning environment. However, this platform faces many challenges, such as the increasing amount of data, the diversity of pedagogical resources and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to take the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a distributed courses recommender system for the e-learning platform. It aims to discover relationships between student’s activities using association rules method in order to help the student to choose the most appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem. The experimental results show the effectiveness and scalability of the proposed system. Finally, we evaluate the performance of Spark MLlib library compared to traditional machine learning tools including Weka and R.

How to effectively combine modern digital technology and the results of the experience accumulated in the traditional party building work, effectively solve the problems existing in the traditional party building work, so that the party building work carried out transparently and efficiently has become a new issue and challenge. In this paper, the overall framework of an intelligent party building system is designed based on the necessity of digital transformation of building activities and performance requirements. The system is divided into four modules: party building management, service management, statistical analysis, and system management, and the functions of each module have been designed and integrated. Based on the principles of database design, a conceptual structure design is proposed, and the association rules and FP-growth algorithm are utilized to perform association mining on party-building activity data. The results of the association rule mining test show that the fluctuation range of confidence and support is between [0.9148, 0.9587] and [0.9048, 0.9348], respectively, and the system passed the association rule mining test. The majority of people believe that using the party building information platform is beneficial for conducting party-building activities. 80% of the users think that the party building system platform is better and meets their needs for use.

The fundamental goal of this work is to prepare and carry out diabetes prediction using various Machine Learning techniques and conduct output analysis of those techniques to find the best classifier with the highest accuracy. This study use the Pima Indian Diabetes Dataset and applied the Machine Learning classification methods like Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) for diabetes prediction. The performance of each algorithm is analysed to determine the one with the best accuracy. The dataset includes details like pregnancies, glucose levels, blood pressure, and other important health information. The focus of this study is to unify FP-Growth algorithm with ML algorithm in order to predict diabetes. The FP-Growth is used to extract the frequent items for data pre-processing before prediction. LR algorithm stands out with high accuracy, showing promise in predicting type 2 diabetes when using the risk factors identified by FP-Growth algorithm. The results help guide future research and make it easier to choose the best algorithms, especially ones that are fast, for medical decision support systems. LR algorithm stands out with high accuracy, showing promise in predicting type 2 diabetes when using the risk factors identified by FP-Growth algorithm.

Using big data analysis technology to build a professional knowledge point network is one of the hotspots of current teaching research. This paper describes the advantages of mining students’ knowledge point learning behavior data in the process of literature learning. By identifying the connotations of knowledge point models, knowledge point judgment methods, and association rule mining methods, the overall process of association rule mining for literature knowledge points is constructed. The 208 knowledge points from the next book of History of Modern Chinese Literature 1915-2022 (Fourth Edition) have been extracted for association rule mining and parameter analysis among the knowledge points in the textbook. Based on frequent patterns and 6000 test questions in the examination system of History of Modern Chinese Literature, association rules between simultaneous right and wrong answers are mined, and associations between related knowledge points of test questions are analyzed. Through the calculation, it is obtained that a knowledge point is associated with about 5 knowledge points on average, and most of the knowledge points in the knowledge network of the textbook can be directly associated with 2 or more knowledge points, and there is more than one way of association. Knowing that the mastery of the knowledge points in the pre-test questions will affect the accuracy of the answers to the post-test questions, teachers can adjust their teaching planning according to the mastery of students’ knowledge points. Constructing a knowledge base network on modern and contemporary literature can effectively improve students’ learning efficiency and facilitate teachers in providing personalized teaching.

Data mining is an important technology to reveal the patterns from crime data. Although there are many researches about this topic, less work models the relations between rates of different kinds of crime. In this paper, an algorithm based on fuzzy association rules (AR) mining is proposed to discover these relations. Two datasets, which are crimes in Chicago from 2012 to 2017 and crimes in NSW from 2008 to 2012, are used for case studies. At first, crime data is preprocessed, where every kind of crime occurring in every district during every month is counted. For a crime in a combination of district and month, the membership function, which is based on hypothesis testing, is designed to evaluate the degree to which its rate is high, normal or low, and the fuzzy transactional dataset is formed. A bridge between fuzzy transactional dataset and binary AR mining algorithm is built, so those mature tools of binary AR mining can be applied to generate fuzzy ARs. In the results of case studies, the strong relations between rates of different crime can be found. There are many interesting and surprise rules, which are worthy to be further studied by domain experts.

Association rule mining plays a critical role in uncovering item correlations and hidden patterns within transactional data, particularly in e-commerce environments. Despite the widespread use of Apriori and FP-Growth algorithms, few studies offer a statistically rigorous, tool-based comparison of their performance on real-world e-commerce data. This paper addresses this gap by evaluating both algorithms in terms of execution time, memory consumption, rule generation volume, and rule strength (support, confidence, and lift). Implementations in RapidMiner and an analysis through SPSS establish statistically significant performance differences, particularly under varying support thresholds. Our findings confirm that FP-Growth consistently outperforms Apriori for large-scale datasets due to its ability to bypass candidate generation, while Apriori retains pedagogical and small-scale relevance. The study contributes practical guidance for data scientists and e-commerce practitioners choosing suitable rule-mining techniques based on their data size and performance constraints.

Incidents are caused by a variety of factors, and there are correlations between incident causative factors. How to effectively clarify the importance of incidental injury-causing factors and their correlations is the current technical challenge in the field of incident causation analysis. This paper takes the study of injury-causing factors and their relationships between object-striking incidents in the process of construction as an example, and it statistically analyzes the incident investigation reports of 126 cases of object-striking incidents in construction projects in China from 2016 to 2022; it screens out 52 categories of incident-causing factors. The Apriori algorithm and FP-growth algorithm are used to data mine the influencing factors obtained from the 126 object-striking incidents: 28 main incident causative items of object-striking incidents and the respective correlation degree between each factor are obtained. By analyzing the support degree of the main incident causation items, as well as comparing and analyzing the results of the incident causation support degree and association rules with Bayesian inference, 9 key injury-causing factors of object-striking incidents are identified. The research results put forward a new research idea for the analysis of the injury factors of object-striking incidents in construction, which can provide theoretical reference for improving the pertinence and effectiveness of incident prevention measures.