Association Rule Mining Analysis Research Articles

Establishment of Safety Management Measures for Major Construction Workers through the Association Rule Mining Analysis of the Data on Construction Accidents in Korea

Despite increasing industrial advancements, fatal and severe accidents, such as “Falls”, “Struck-by”, “Hit by an object”, “Be crushed”, and “Caught-in/between” accidents, persist in developed countries, including Korea. Various methods, including risk assessment, monitoring systems, technology improvements, and safety education, are being implemented to reduce accidents. However, only a few studies have revealed the causes of accidents and their interrelationships; these studies are based on limited data. Korea recently published accident data using national statistical systems, including the construction safety management integrated information (CSI), enabling the analyses of major accident types. Here, we selected various representative accident cases to minimize the duplication of the data published from 2019 to 2023 and applied the Material, Method, Machine, or Man (4M) analysis method, a risk assessment technique, to perform an accident-type-based association rule mining (ARM) analysis of the accident factors. Through the ARM analysis, we quantitatively identified complex correlations for major accidents. Based on the 4M factors derived through this analysis, we improved a 2–4 model for accident causation and proposed safety management measures for each construction entity.

Exploring the efficacy of Phyllanthus emblica L. based on association rule mining and multidimensional analysis

ABSTRACT Phyllanthus emblica L. (P. emblica L.) is a widely consumed healthy fruit known for its taste and benefits. This study used association rule mining to demonstrate its anticancer properties, analyzing its 65 chemical components, 1767 related targets, and 4282 efficacy items. Myricetin was identified as a key component targeting CA2, which is involved in cancer pathways. The research suggested P. emblica L.’s potential against non-small cell lung cancer and esophageal cancer, which was supported by experiments showing significant growth inhibition in related cancer cells. This approach offers a more targeted and convincing understanding of P. emblica L.’s effects, improving upon traditional food research methods.

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis.

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

Screening potential treatments for mpox from Traditional Chinese Medicine by using a data-driven approach.

Mpox (MPX) has escalated into a public health emergency of international concern, necessitating urgent prophylactic and therapeutic measures. The primary goal of this investigation was to systematically extract Wan Quan's expertise in treating smallpox, as documented in Exclusive Methods for Treating Pox (Dou Zhen Xin Fa in Chinese), with the aim of identifying potential prescriptions, herbs, and components for alternative MPX therapies or drugs. This research utilized data mining to identify high-frequency Chinese Medicines (CMs), high-frequency CM-pairs, and CM compatibility rules. Network pharmacology, molecular docking, and molecular dynamic simulation were employed to reveal the potential molecular mechanisms of the core CM-pair. 119 prescriptions were extracted from Exclusive Methods for Treating Pox. We identified 25 high-frequency CMs and 23 high-frequency CM pairs among these prescriptions. Combined association rule mining analysis, Gancao (Glycyrrhiza uralensis Fisch.), Renshen (Panax ginseng C. A. Mey.), Danggui (Angelica sinensis (Oliv.) Diels), Shengma (Cimicifuga foetida L.), and Zicao (Lithospermum erythrorhizon Siebold & Zucc.) were selected as the core CM-pair for further investigation. Network pharmacology analysis yielded 131 active components and 348 candidate targets for the core CM-pair. Quercetin and celabenzine were chosen as ligands for molecular docking. GO and KEGG enrichment analyses revealed that the core CM-pair could interact with targets involved in immune, inflammatory, and infectious diseases. Moreover, key mpox virus targets, F8-A22-E4 DNA polymerase holoenzyme and profilin-like protein A42R, were docked well with the selected core components. And molecular dynamic simulation indicated that the component (quercetin) could stably bind to the target (profilin-like protein A42R). Our findings identified potential prescriptions, herbs, and components that can offer potential therapies or drugs for addressing the MPX epidemic.

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining.

Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field. Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas. The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene. Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline.

Analysis of Factors Affecting the Over-Representation of Sequential Crashes in Freeway Tunnels: Using Rule-Based Data Mining Method

The paper provides an empirical analysis of road/tunnel design, traffic volume, and environmental factors associated with the increased likelihood of sequential crashes in freeway tunnels. The association rule mining and decision tree methods are employed since both of them are capable of identifying complicated interactions among variables and expressing them in the form of rules. Results show that tunnel length, traffic congestion, time of day, season, and vehicle type are the significant factors influencing the likelihood of sequential crashes in freeway tunnels. More importantly, association rule mining and decision tree analysis reveal that a combination of road/tunnel design, traffic, and environmental factors produces even a higher likelihood of sequential crashes, leading to a series of hazardous situations. For example, when factors including long tunnel and grade ≤ 2%, fourth level, and winter are combined, the proportion of sequential crashes is more than twice the average proportion of sequential crashes in the complete tunnel crash database. Traffic safety management should pay more attention to monitoring these hazardous situations which are more likely to be linked to sequential crashes.

Effect of Chinese herbal medicine therapy on risks of all-cause mortality, infections, parasites, and circulatory-related mortality in HIV/AIDS patients with neurological diseases.

Introduction: Long-term living with human immunodeficiency virus (HIV) and/or antiretroviral therapy (ART) is associated with various adverse effects, including neurocognitive impairment. Heterogeneous neurocognitive impairment remains an important issue, affecting between 15-65% of human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS) patients and resulting in work performance, safety, and health-related outcomes that have a heavy economic burden. Methods: We identified 1,209 HIV/AIDS patients with neurological diseases during 2010-2017. The Kaplan-Meier method, log-rank test, and Cox proportional hazards model were used to analyze 308 CHM users and 901 non-CHM users within this population. Major CHM clusters were determined using association rule mining and network analysis. Results and Discussion: Results showed that CHM users had a 70% lower risk of all-cause mortality (adjusted hazard ratio (aHR) = 0.30, 95% confidence interval (CI):0.16-0.58, p < 0.001) (p = 0.0007, log-rank test). Furthermore, CHM users had an 86% lower risk of infections, parasites, and circulatory-related mortality (aHR = 0.14, 95% confidence interval (CI):0.04-0.46, p = 0.001) (p = 0.0010, log-rank test). Association rule mining and network analysis showed that two CHM clusters were important for patients with neurological diseases. In the first CHM cluster, Huang Qin (HQ; root of Scutellaria baicalensis Georgi), Gan Cao (GC; root of Glycyrrhiza uralensis Fisch.), Huang Lian (HL; root of Coptis chinensis Franch.), Jie Geng (JG; root of Platycodon grandiflorus (Jacq.) A.DC.), and Huang Bai (HB; bark of Phellodendron amurense Rupr.) were identified as important CHMs. Among them, the strongest connection strength was identified between the HL and HQ. In the second CHM cluster, Suan-Zao-Ren-Tang (SZRT) and Ye Jiao Teng (YJT; stem of Polygonum multiflorum Thunb.) were identified as important CHMs with the strongest connection strength. CHMs may thus be effective in treating HIV/AIDS patients with neurological diseases, and future clinical trials are essential for the prevention of neurological dysfunction in the population.

Prediction of contraceptive discontinuation among reproductive-age women in Ethiopia using Ethiopian Demographic and Health Survey 2016 Dataset: A Machine Learning Approach

BackgroundGlobally, 38% of contraceptive users discontinue the use of a method within the first twelve months. In Ethiopia, about 35% of contraceptive users also discontinue within twelve months. Discontinuation reduces contraceptive coverage, family planning program effectiveness and contributes to undesired fertility. Hence understanding potential predictors of contraceptive discontinuation is crucial to reducing its undesired outcomes. Predicting the risk of discontinuing contraceptives is also used as an early-warning system to notify family planning programs. Thus, this study could enable to predict and determine the predictors for contraceptive discontinuation in Ethiopia.MethodologySecondary data analysis was done on the 2016 Ethiopian Demographic and Health Survey. Eight machine learning algorithms were employed on a total sample of 5885 women and evaluated using performance metrics to predict and identify important predictors of discontinuation through python software. Feature importance method was used to select top predictors of contraceptive discontinuation. Finally, association rule mining was applied to discover the relationship between contraceptive discontinuation and its top predictors by using R statistical software.ResultRandom forest was the best predictive model with 68% accuracy which identified the top predictors of contraceptive discontinuation. Association rule mining identified women's age, women’s education level, family size, husband’s desire for children, husband’s education level, and women’s fertility preference as predictors most frequently associated with contraceptive discontinuation.ConclusionResults have shown that machine learning algorithms can accurately predict the discontinuation status of contraceptives, making them potentially valuable as decision-support tools for the relevant stakeholders. Through association rule mining analysis of a large dataset, our findings also revealed previously unknown patterns and relationships between contraceptive discontinuation and numerous predictors.

Patterns and Determinants of Multimorbidity in Older Adults: Study in Health-Ecological Perspective.

(1) Background: Multimorbidity has become one of the key issues in the public health sector. This study aims to explore the patterns and health-ecological factors of multimorbidity in China to propose policy recommendations for the management of chronic diseases in the elderly. (2) Methods: A multi-stage random sampling method was used to conduct a questionnaire survey on 3637 older adults aged 60 and older in Shanxi, China. Association rule mining analysis (ARM) and network analysis were applied to analyze the patterns of multimorbidity. The health-ecological model was adopted to explore the potential associated factors of multimorbidity in a multidimensional perspective. A hierarchical multiple logistic model was employed to investigate the association strengths reflected by adjusted odds ratios and 95% confidence. (3) Results: Multimorbidity occurred in 20.95% of the respondents. The graph of network analysis showed that there were 6 combinations of chronic diseases with strong association strengths and 14 with moderate association strengths. The results of the ARM were similar to the network analysis; six dyadic chronic disease combinations and six triadic ones were obtained. Hierarchical multiple logistic regression indicated that innate personal traits (age, history of genetics, and body mass index), behavioral lifestyle (physical activity levels and medication adherence), interpersonal network (marital status), and socioeconomic status (educational level) were the common predictors of multimorbidity for older adults, among which, having no family history was found to be a relative determinant as a protective factor for multimorbidity after controlling the other covariates. (4) Conclusions: multimorbidity was prevalent in older adults and most disease combinations are associated with hypertension, followed by diabetes. This shows that diabetes and hypertension have a high prevalence among older adults and have a wide range of associations with other chronic diseases. Exploring the patterns and associated factors of multimorbidity will help the country prevent complications and avoid the unnecessary use of the health service, adopting an integrated approach to managing multimorbidity rather than an individual disease-specific approach and implementing different strategies according to the location of residence.

Exploration on prior driving modes for automated vehicle collisions

ABSTRACT The emergence of automated vehicles (AV) has been occurring rapidly as these vehicles have the potential to reduce/eradicate human driving faults and related collisions. To enhance AV safety, the NHTSA recommends the continuous presence of a backup human driver that has a reasonable understanding of AV technologies to ensure disengagement when manual overtake is required. However, due to several AV-related traffic crashes during roadway testing and extensive media interest, AV safety has become a critical issue. This study collected 255 crash reports filed by different manufacturers testing AVs in California from September 2014 to April 2020. The crash dataset was analyzed using two data mining algorithms (association rule mining and text network analysis) to identify the key AV-related crash attributes and their associations based on the vehicle’s prior driving mode (conventional or automated). The results show that the manner of collision and the prior movement of the testing vehicle are strongly connected with prior driving mode. For example, AV crashes in a manual driving mode often result in a sideswipe collision during moving status, whereas AV crashes in an automated driving mode are highly associated with rear-end collisions when AVs are stopped in traffic. The findings of this study can help policymakers and AV engineers improve AV deployment strategies to support the adoption of AVs and promote potential safety benefits for AV technologies.

Panels of mRNAs and miRNAs for decoding molecular mechanisms of Renal Cell Carcinoma (RCC) subtypes utilizing Artificial Intelligence approaches

Renal Cell Carcinoma (RCC) encompasses three histological subtypes, including clear cell RCC (KIRC), papillary RCC (KIRP), and chromophobe RCC (KICH) each of which has different clinical courses, genetic/epigenetic drivers, and therapeutic responses. This study aimed to identify the significant mRNAs and microRNA panels involved in the pathogenesis of RCC subtypes. The mRNA and microRNA transcripts profile were obtained from The Cancer Genome Atlas (TCGA), which were included 611 ccRCC patients, 321 pRCC patients, and 89 chRCC patients for mRNA data and 616 patients in the ccRCC subtype, 326 patients in the pRCC subtype, and 91 patients in the chRCC for miRNA data, respectively. To identify mRNAs and miRNAs, feature selection based on filter and graph algorithms was applied. Then, a deep model was used to classify the subtypes of the RCC. Finally, an association rule mining algorithm was used to disclose features with significant roles to trigger molecular mechanisms to cause RCC subtypes. Panels of 77 mRNAs and 73 miRNAs could discriminate the KIRC, KIRP, and KICH subtypes from each other with 92% (F1-score ≥ 0.9, AUC ≥ 0.89) and 95% accuracy (F1-score ≥ 0.93, AUC ≥ 0.95), respectively. The Association Rule Mining analysis could identify miR-28 (repeat count = 2642) and CSN7A (repeat count = 5794) along with the miR-125a (repeat count = 2591) and NMD3 (repeat count = 2306) with the highest repeat counts, in the KIRC and KIRP rules, respectively. This study found new panels of mRNAs and miRNAs to distinguish among RCC subtypes, which were able to provide new insights into the underlying responsible mechanisms for the initiation and progression of KIRC and KIRP. The proposed mRNA and miRNA panels have a high potential to be as biomarkers of RCC subtypes and should be examined in future clinical studies.

Assessing the Vulnerability of Cancer Patients for COVID-19.

Severe acute respiratory syndrome involving corona virus-2 (SARS-CoV-2) has been implied to cause COVID-19 disease, leading to an unprecedented health emergency across the globe with a staggering figure of mortality rate. Measures to control the pandemic are pushing the economy into a tailspin, putting burden not only on the individuals but also on the nations. Despite the widespread infection rates, young people have shown better recovery rate while COVID-19 symptoms are more pronounced in elderly and people with comorbid conditions such as diabetes, cardiac and respiratory diseases. Cancer is a highly prevalent disease affecting millions of individuals. In this study, we analyzed the expression status of genes that are required for SARS-CoV-2 infectivity and its propagation to assess the susceptibility of certain cancer patients to infection and subsequent complications. Our data indicate that patients with colon, rectum, cholangiocarcinoma, lung adenoma, kidney renal papillary cell carcinoma and kidney renal clear cell carcinoma are more at risk for COVID-19. Genes that are responsible for severe COVID-19 are also highly expressed in many cancer types. We also carried out the association rule mining analysis which is helpful in predicting the expression of proviral genes in various cancers.

Prevalence and patterns of multimorbidity in chronic diseases in Guangzhou, China: a data mining study in the residents’ health records system among 31 708 community-dwelling elderly people

ObjectivesExamination of the prevalence, influence factors and patterns of multimorbidity among the elderly people in Guangzhou, China.DesignCross-sectional study.Participants31 708 community-dwelling elderly people aged 65 and over.Primary and secondary outcome measuresPrevalence,...

Mining of the Association Rules Between Socio-Economic Development Indicators and Rural Harmless Sanitary Toilet Penetration Rate to Inform Sanitation Improvement in China

The imbalance of socioeconomic development (SED) in different regions of China has resulted in the variability of rural infrastructure penetration. This study aims to improve the SED of each region in China to increase the penetration rate of rural harmless sanitary toilets (RHST). For this purpose, we used association rule mining to analyze the relationship between SED indicators and the penetration rate of RHST for proposing differentiated improvement strategies. Population urbanization rate, tertiary vs. secondary industry output ratio, nonagricultural output value ratio, nonagricultural employment ratio, per capita gross domestic product, and the proportion of added value of industry in the total added value of commodity were used to measure the SED level of 30 regions in China from 2007 to 2017. Results showed that the SED of each region has been improving, and the proportion of added value of industry in the total added value of commodity accounted for the highest proportion of SED. The penetration rate of RHST in each region increased continuously but with significant variability from 2007 to 2017. The range of six SED indicators corresponding to high and low penetration rates of RHST was determined by association rule mining analysis. On the basis of the degree of RHST penetration rate by region in China in 2017 as a reference, differentiated measures were proposed to improve the penetration of RHST in different regions.

Efficiency and Stability Analysis of 2D/3D Perovskite Solar Cells Using Machine Learning

A dataset containing 599 data points from 146 publications on 2D/3D perovskite solar cells is analyzed using machine learning. The predictive models are developed for power conversion efficiency (PCE) using eXtreme Gradient Boosting regression, random forest regression and artificial neural networks while association rule mining is used to analyze the stability data to identify the descriptors leading to high stability 2D/3D cells. A predictive model is also developed for the bandgap to predict the missing values in the dataset for the use in PCE predictions. Models for both bandgap and PCE predictions are quite successful. The thickness of inorganic layer (n), radius of anion (R x ), and 2D cation (R m) are found to be the most important descriptors for bandgap predictions; n and R m, together with the bandgap, are found to be deterministic for PCE in regular cells while the bandgap, n, and conduction band energy of hole transport layer are the most influential descriptors in inverted structures. Association rule mining analysis for the stability indicates that the cells with layered perovskite structures are more stable while the 2D and 3D cations leading to the most stable cells are found to be butylammonium and formamidinium‐Cs mixed cation respectively.

Frequency and types of clusters of major chronic diseases in 0.5 million adults in urban and rural China.

BackgroundLittle is known about the frequency and types of disease clusters involving major chronic diseases that contribute to multimorbidity in China. We examined the frequency of disease clusters involving major chronic diseases and their relationship with age and socioeconomic status in 0.5 million Chinese adults.MethodsMultimorbidity was defined as the presence of at least two or more of five major chronic diseases: stroke, ischaemic heart disease (IHD), diabetes, chronic obstructive pulmonary disease (COPD) and cancer. Multimorbid disease clusters were estimated using both self-reported doctor-diagnosed diseases at enrolment and incident cases during 10-year follow-up. Frequency of multimorbidity was assessed overall and by age, sex, region, education and income. Association rule mining (ARM) and latent class analysis (LCA) were used to assess clusters of the five major diseases.ResultsOverall, 11% of Chinese adults had two or more major chronic diseases, and the frequency increased with age (11%, 24% and 33% at age 50–59, 60–69 and 70–79 years, respectively). Multimorbidity was more common in men than women (12% vs 11%) and in those living in urban than in rural areas (12% vs 10%), and was inversely related to levels of education. Stroke and IHD were the most frequent combinations, followed by diabetes and stroke. The patterns of self-reported disease clusters at baseline were similar to those that were recorded during the first 10 years of follow-up.ConclusionsCardiometabolic and cardiorespiratory diseases were most common disease clusters. Understanding the nature of such clusters could have implications for future prevention strategies.

Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Asthma Based on Data Mining

Objective: Asthma is a highly prevalent respiratory disease that remains difficult to control. Acupuncture, as an important alternative therapeutic modality in preventing and treating asthma, is widely used in the world due to its promising efficacy and safety. Although acupoint selection and combinations are critical to therapeutic effects of acupuncture, its fundamental rules for asthma have not been fully understood. Thus, using data mining, the present study aimed to discover the most effective acupoints and combinations in the acupuncture treatment of asthma. Methods: Controlled clinical trials (CCTs) of acupuncture treatment for asthma were searched and retrieved from databases including Chinese National Knowledge Infrastructure (CNKI), Wanfang, and PubMed. Data regarding the main acupoints prescribed in these clinical trials was collected and quantified. A network analysis was performed to uncover the interconnections between the acupoints. Additionally, hierarchical clustering analysis and association rule mining were conducted to discover the potential acupoint combinations. Results: A total of 183 CCTs were retrieved. Feishu (BL13), Dingchuan (EX-B1), Dazhui (GV14), Shengshu (BL23), Pishu (BL20), and Fengmen (BL12) appeared to be the most frequently used acupoints for asthma. While the Bladder Meridian of Foot Taiyang, the Governor Vessel, and the Conception Vessel, compared to other meridians, were found to be the more commonly selected meridians. In the acupoint interconnection network, Feishu (BL13), Fengmen (BL12), Dingchuan (EX-B1), and Dazhui (GV14) were defined as key node acupoints. Moreover, acupoint clustering analysis revealed the treatment principle of “facilitating the flow of the lung Qi, tonifying spleen and kidney, and treating both the symptoms and root causes.” Association rule mining analysis demonstrated that the combination of Pishu, Shenshu, Feishu, and Dingchuan, as well as that of Feishu, Dazhui, and Fengmen were potential acupoint combinations that should be selected with priority in asthma treatment. Conclusion: Based on a data mining analysis of published CCTs, this study provides valuable information regarding the selection of the most effective acupoints and combinations for clinical acupuncture practice and experimental study aimed at the prevention and treatment of asthma.

East Asian herbal medicine for cancer pain: A protocol for systematic review and meta-analysis with using association rule analysis to identify core herb pattern.

Cancer pain is an important factor in cancer management that affects a patient's quality of life and survival-related outcomes. The aim of this review is to systematically evaluate the efficacy and safety of oral administration East Asian herbal medicine (EAHM) for primary cancer pain, and to explore core herb patterns based on collected data. A comprehensive literature search will be conducted in 10 electronic databases including PubMed, Cochrane Library, Cumulative Index to Nursing & Allied Health Literature, EMBASE, Korean Studies Information Service System, Research Information Service System Oriental Medicine Advanced Searching Integrated System, Korea Citation Index, Chinese National Knowledge Infrastructure Database (CNKI), CiNii for randomized controlled trials from their inception until August 19, 2021. Statistical analysis will be performed in the software R version 4.1.1. and R studio program using the default settings of the 'meta' package. When heterogeneity in studies is detected, the cause will be identified through meta regression and subgroup analysis. Methodological quality will be assessed independently using the revised tool for risk of bias in randomized trials (Rob 2.0). This study will provide more comprehensive and specific evidence of EAHM for cancer pain management. Based on the results of this review, it is expected that the efficacy and safety of East Asian herbal medicine for cancer pain may be confirmed. In addition, it will be possible to derivation of a core herb pattern related to this research topic through additional association rule mining analysis.

Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining

The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R2, 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year.

Correlation Analysis of External Environment Risk Factors for High-Speed Railway Derailment Based on Unstructured Data

In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.