Search Results (86)

Outlier detection is pivotal in data mining and machine learning, as it focuses on discovering unusual behaviors that deviate substantially from the majority of data samples. Conventional approaches, however, often falter when dealing with complex data that are multimodal or sparse or that exhibit strong nonlinearity. To address these challenges, this paper introduces a novel outlier detection framework named Multimodal Granular Distance-based Outlier Detection (MGDOD), which leverages granular computing principles in conjunction with multimodal granulation techniques. Specifically, similarity measures and granulation methods are employed to generate granules from single-modal data, thereby reducing inconsistencies arising from different data modalities. These granules are then combined to form multimodal granular vectors, whose size, measurement, and operational rules are carefully defined. Building on this conceptual foundation, we propose two multimodal granular distance measures, which are formally axiomatized, and develop an associated outlier detection algorithm. Experimental evaluations on benchmark datasets from UCI, ODDS, and multimodal sources compare the proposed MGDOD method against established outlier detection techniques under various granulation parameters, distance metrics, and outlier conditions. The results confirm the effectiveness and robustness of MGDOD, demonstrating its superior performance in identifying anomalies across diverse and challenging data scenarios. Full article

Background: Justice-involved adolescents exhibit high rates of mental health disorders with complex comorbidity patterns. Understanding these patterns is crucial for developing targeted interventions in this vulnerable population. Methods: We applied multiple machine-learning techniques to electronic records from 124 justice-involved adolescents (11–21 years; mean = 15.7 ± 1.9). Analyses included association rule mining, K-Means clustering with t-SNE visualization, and topic modeling of clinicians’ recommendation notes. Results: Hyperkinetic disorders (F90.0/F90.1) and family-stress factors (Z63.5) together accounted for approximately 45% of all ICD-10 entries. A four-cluster K-Means solution built on age + F-codes alone showed weak separation (silhouette = 0.044), whereas adding Z-codes markedly improved cohesion (silhouette = 0.468) and isolated a distinct hyperkinetic–family-stress subgroup. Association-rule mining returned one robust rule, F81 → F90.0 (support = 0.048, confidence = 0.46, lift = 1.59), underscoring the frequent co-diagnosis of learning and attention-deficit disorders. Topic modeling of clinicians’ recommendation notes recovered five coherent intervention themes—vocational guidance, parent counseling, psycho-education, family psychotherapy, and psychiatric follow-up—which aligned closely with the data-driven clusters. Conclusions: These findings demonstrate how routine clinical data can reveal actionable comorbidity profiles and guide tailored interventions for complex adolescent mental-health presentations. Full article

The emergence of the Internet of Things (IoT) has brought about a significant technological shift, coupled with the rise of intelligent computing. IoT integrates various digital and analogue devices with the Internet, enabling advanced communication between devices and humans.The pervasive adoption of IoT has transformed urban infrastructures into interconnected smart cities. Here, we propose a framework that mathematically models and automates power consumption management for IoT devices in smart city environments ranging from residential buildings to healthcare settings. The proposed framework utilises set theoretic association-rule mining and combines unsupervised preprocessing with frequent-item set mining and iterative numerical optimisation to reduce non-critical energy consumption. Readings are first converted into binary transaction matrices; then a modified Apriori algorithm is applied to extract high-confidence usage patterns and association rules. Dimensionality reduction techniques compress these transaction profiles, while the Gauss–Seidel method computes control set points that balance energy efficiency. The resulting rule set is deployed through a web portal that provides real-time device status, remote actuation, and automated billing. These associative rules generate predictive control functions, optimise the response of the framework, and prepare the framework for future events. A web portal is introduced that enables remote control of IoT devices and facilitates power usage monitoring, as well as automated billing. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure. Full article

With the rapid advancement of digitalization and intelligence in power systems, SF₆ high-voltage circuit breakers, as the core switching devices in power grid protection systems, have become critical components in high-voltage networks of 110 kV and above due to their superior insulation performance and exceptional arc-quenching capability. Their operational status directly impacts the reliability of power system protection. Therefore, real-time condition monitoring and accurate assessment of SF₆ circuit breakers along with science-based maintenance strategies derived from evaluation results hold significant engineering value for ensuring secure and stable grid operation and preventing major failures. In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF₆ high-voltage circuit breakers. To address this, we propose an operational status assessment model for SF₆ high-voltage circuit breakers based on an Integrated Attribute-Weighted Risk Model Based on the Branch–Trunk Rule (IAR-BTR), which integrates internal and environmental influences. Firstly, to tackle the issues of incomplete data and feature imbalance caused by irrelevant attributes, this study employs missing value elimination (Drop method) on the fault record database. The selected dataset is then normalized according to the input feature matrix. Secondly, conventional risk factors are extracted using traditional association rule mining techniques. To improve the accuracy of these rules, the filtering thresholds and association metrics are refined based on seasonal distribution and the importance of time periods. This allows for the identification of spatiotemporally non-stationary factors that are strongly correlated with circuit breaker failures in low-probability seasonal conditions. Finally, a quantitative weighting method is developed for analyzing branch-trunk rules to accurately assess the impact of various factors on the overall stability of the circuit breaker. The DFP-Growth algorithm is applied to enhance the computational efficiency of the model. The case study results demonstrate that the proposed method achieves exceptional accuracy (95.78%) and precision (97.22%) and significantly improves the predictive performance of SF₆ high-voltage circuit breaker operational condition assessments. Full article

This study employs text mining techniques to conduct a systematic quantitative analysis of cybersecurity-related scientific publications and technological research. It aims to break through the limitations of traditional unidirectional evolutionary research, reveal the knowledge evolution rules between scientific theories and technical practices in this field, and provide valuable references and decision-making support for optimizing the collaborative innovation ecosystem. Firstly, we took academic papers and patent research on cybersecurity from 2005 to 2024 as the research objects and divided them into ten stages according to the time series. Subsequently, we identified scientific and technological topics and formed science–technology topics to assess their similarity. Then, we selected 3040 pairs of collaborative topic pairs and categorized them into three distinct groups: weak, moderate, and strong correlation. Finally, we constructed a science–technology topic association evolution atlas and analyzed the types of evolutionary pathways of topic associations and their mechanisms of action accordingly. The results demonstrate five evolutionary patterns in science–technology topic associations: division, merging, inheritance, co-occurrence, and independent development. Additionally, the science–technology topics demonstrate a high degree of collaboration, exhibiting a collaborative effect of “initial accumulation–fluctuating differentiation–deep collaboration”. Meanwhile, the correlation evolution of strongly related science–technology topics presents a symmetrical effect of “technology–science–technology” and “science–technology/technology–science”. Full article

Drug–drug interactions (DDIs) can pose significant risks in clinical practice and pharmacovigilance. Although traditional association rule mining techniques, such as the Apriori algorithm, have been applied to drug safety signal detection, their performance in DDI detection has not been systematically evaluated, especially in the Spontaneous Reporting System (SRS), which contains a large number of drugs and AEs with a complex correlation structure and unobserved latent factors. This study fills that gap through comprehensive simulation studies designed to mimic key features of SRS data. We show that latent confounding can substantially distort detection accuracy: for example, when using the reporting ratio (RR) as a secondary indicator, the area under the curve (AUC) for detecting main effects dropped by approximately 30% and for DDIs by about 15%, compared to settings without confounding. A real-world application using 2024 VAERS data further illustrates the consequences of unmeasured bias, including a potentially spurious association between COVID-19 vaccination and infection. These findings highlight the limitations of existing methods and emphasize the need for future tools that account for latent factors to improve the reliability of safety signal detection in pharmacovigilance analyses. Full article

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. Full article

This study introduces a novel Recency, Monetary, and Duration (RMD) model for customer classification in the hospitality industry. Using a hybrid approach that integrates data mining with multi-criteria decision-making techniques, this study aims to identify valuable customer segments and optimize marketing strategies. This research applies the K-means clustering algorithm to classify customers from a hotel in Iran based on RMD attributes. Cluster validation is performed using three internal indices, and hidden patterns are extracted through association rule mining. Customer segments are prioritized using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and Customer Lifetime Value (CLV) analysis. The outcomes revealed six distinct customer clusters, identified as new customers; loyal customers; collective buying customers; potential customers; business customers, and lost customers. This study helps hotels to be aware of different types of customers with particular spending patterns, enabling hotels to tailor services and improve customer retention. It also provides managers with appropriate tools to allocate resources efficiently. This study extends the traditional Recency, Frequency, and Monetary (RFM) model by incorporating duration, an overlooked dimension of customer engagement. It is the first attempt to integrate data mining and multi-criteria decision-making for customer segmentation in Iran’s hospitality industry. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

Location-based social networks (LBSNs) leverage geo-location technologies to connect users with places, events, and other users nearby. Using GPS data, platforms like Foursquare enable users to check into locations, share their locations, and receive location-based recommendations. A significant research gap in LBSNs lies in the limited exploration of users’ tendencies to withhold certain location data. While existing studies primarily focus on the locations users choose to disclose and the activities they attend, there is a lack of research on the hidden or intentionally omitted locations. Understanding these concealed patterns and integrating them into predictive models could enhance the accuracy and depth of location prediction, offering a more comprehensive view of user mobility behavior. This paper solves this gap by proposing an Associative Hidden Location Trajectory Prediction model (AHLTP) that leverages user trajectories to infer unchecked locations. The FP-growth mining technique is used in AHLTP to extract frequent patterns of check-in locations, combined with machine-learning methods such as K-nearest-neighbor, gradient-boosted-trees, and deep learning to classify hidden locations. Moreover, AHLTP uses association rule mining to derive the frequency of successive check-in pairs for the purpose of hidden location prediction. The proposed AHLTP integrated with the machine-learning models classifies the data effectively, with the KNN attaining the highest accuracy at 98%, followed by gradient-boosted trees at 96% and deep learning at 92%. Comparative study using a real-world dataset demonstrates the model’s superior accuracy compared to state-of-the-art approaches. Full article

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article

Sports analytics is a fast-evolving domain using advanced data science methods to find useful insights. This study explores the way NBA player performance metrics evolve from quarter to quarter and affect game outcomes. Using Association Rule Mining, we identify key offensive, defensive, and overall impact metrics that influence success in both regular-season and playoff contexts. Defensive metrics become more critical in late-game situations, while offensive efficiency is paramount in the playoffs. Ball handling peaks in the second quarter, affecting early momentum, while overall impact metrics, such as Net Rating and Player Impact Estimate, consistently correlate with winning. In the collected dataset we performed preprocessing, applying advanced anomaly detection and discretization techniques. By segmenting performance into five categories—Offense, Defense, Ball Handling, Overall Impact, and Tempo—we uncovered strategic insights for teams, coaches, and analysts. Results emphasize the importance of managing player fatigue, optimizing lineups, and adjusting strategies based on quarter-specific trends. The analysis provides actionable recommendations for coaching decisions, roster management, and player evaluation. Future work can extend this approach to other leagues and incorporate additional contextual factors to refine evaluation and predictive models. Full article

Geochemical data serve as crucial references for prospecting, and the effective extraction of prospecting information from such data determines the success rate of exploration. In the era of big data, novel prospecting methods based on geochemical data offer new ideas for exploring various ore deposits. By employing advanced data analysis techniques like machine learning and artificial intelligence, it becomes possible to identify elusive patterns and trends that are challenging to detect using traditional approaches, thereby significantly enhancing the success rate of prospecting endeavors. In this study, we selected drainage sediment geochemical data (Au, Ba, Mo, Sb, V, W, Zn) in the Jinya-Mingshan area to explore potential Carlin-type gold deposits. Traditional geochemical processing methods along with an association rule algorithm were employed for conducting comprehensive data mining analysis. The results demonstrate that the element combinations within the study area can be categorized into strong positive associations and enrichments (Mo, Sb, Zn) associated with vulcanization, strong negative associations, and decarbonation-related migration elements (Ba), as well as strong positive associations and weakly enriched elements (W) and weak positive associations and weakly enriched elements (V) not significantly related to mineralization. In comparison to Mo and Sb, which are closely linked to Au as revealed by cluster analysis and factor analysis, the association rule algorithm also reveals a relatively close correlation between Ba, Zn, and Au. Based on the element correlations obtained through the association rule algorithm, a new prospecting index was constructed for the study area. This new index is more reasonable than traditional indices. In conclusion, the association rule algorithm possesses unique advantages in information mining of geochemical data and holds promising applications in geological exploration. Full article