Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

IntroductionHospitals have seen a rise in Medical Emergency Team (MET) reviews. We hypothesised that the commonest MET calls result in similar treatments. Our aim was to design a pre-emptive management algorithm that allowed direct institution of treatment to patients without having to wait for attendance of the MET team and to model its potential impact on MET call incidence and patient outcomes.MethodsData was extracted for all MET calls from the hospital database. Association rule data mining techniques were used to identify the most common combinations of MET call causes, outcomes and therapies.ResultsThere were 13,656 MET calls during the 34-month study period in 7936 patients. The most common MET call was for hypotension [31%, (2459/7936)]. These MET calls were strongly associated with the immediate administration of intra-venous fluid (70% [1714/2459] v 13% [739/5477] p

We discuss a statistical framework that underlies envelope detection schemes as well as dynamical models based on Hidden Markov Models (HMM) that can encompass both discrete and continuous sensor measurements for use in Integrated System Health Management (ISHM) applications. The HMM allows for the rapid assimilation, analysis, and discovery of system anomalies. We motivate our work with a discussion of an aviation problem where the identification of anomalous sequences is essential for safety reasons. The data in this application are discrete and continuous sensor measurements and can be dealt with seamlessly using the methods described here to discover anomalous flights. We specifically treat the problem of discovering anomalous features in the time series that may be hidden from the sensor suite and compare those methods to standard envelope detection methods on test data designed to accentuate the differences between the two methods. Identification of these hidden anomalies is crucial to building stable, reusable, and cost-efficient systems. We also discuss a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an ISHM system. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, sear- ching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and algorithms for P2P environments. Next it discusses some of the privacy concerns with P2P data mining and points out the problems of existing privacy-preserving multi-party data mining techniques. It further points out that most of the nice assumptions of these existing privacy preserving techniques fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). The paper offers a more realistic formulation of the PPDM problem as a multi-party game and points out some recent results.

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

List of Top Schools of International Journal of Data Mining Techniques and Applications sorted by citations.

The purpose of data mining analysis is always to find patterns of the data using certain kind of techiques such as classification or regression. It is not always feasible to apply classification algorithms directly to dataset. Before doing any work on the data, the data has to be pre-processed and this process normally involves feature selection and dimensionality reduction. We tried to use clustering as a way to reduce the dimension of the data and create new features. Based on our project, after using clustering prior to classification, the performance has not improved much. The reason why it has not improved could be the features we selected to perform clustering are not well suited for it. Because of the nature of the data, classification tasks are going to provide more information to work with in terms of improving knowledge and overall performance metrics. From the dimensionality reduction perspective: It is different from Principle Component Analysis which guarantees finding the best linear transformation that reduces the number of dimensions with a minimum loss of information. Using clusters as a technique of reducing the data dimension will lose a lot of information since clustering techniques are based a metric of 'distance'. At high dimensions euclidean distance loses pretty much all meaning. Therefore using clustering as a "Reducing" dimensionality by mapping data points to cluster numbers is not always good since you may lose almost all the information. From the creating new features perspective: Clustering analysis creates labels based on the patterns of the data, it brings uncertainties into the data. By using clustering prior to classification, the decision on the number of clusters will highly affect the performance of the clustering, then affect the performance of classification. If the part of features we use clustering techniques on is very suited for it, it might increase the overall performance on classification. For example, if the features we use k-means on are numerical and the dimension is small, the overall classification performance may be better. We did not lock in the clustering outputs using a random_state in the effort to see if they were stable. Our assumption was that if the results vary highly from run to run which they definitely did, maybe the data just does not cluster well with the methods selected at all. Basically, the ramification we saw was that our results are not much better than random when applying clustering to the data preprocessing. Finally, it is important to ensure a feedback loop is in place to continuously collect the same data in the same format from which the models were created. This feedback loop can be used to measure the model real world effectiveness and also to continue to revise the models from time to time as things change.

The global Data Mining Software market is experiencing robust growth, driven by the increasing need for businesses to extract valuable insights from massive datasets. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors. The burgeoning adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large enterprises and SMEs. Furthermore, advancements in machine learning and artificial intelligence algorithms are enhancing the accuracy and efficiency of data mining processes, leading to better decision-making across various sectors like finance, healthcare, and marketing. The rise of big data analytics and the increasing availability of affordable, high-powered computing resources are also significant contributors to market growth. However, the market faces certain challenges. Data security and privacy concerns remain paramount, especially with the increasing volume of sensitive information being processed. The complexity of data mining software and the need for skilled professionals to operate and interpret the results present a barrier to entry for some businesses. The high initial investment cost associated with implementing sophisticated data mining solutions can also deter smaller organizations. Nevertheless, the ongoing technological advancements and the growing recognition of the strategic value of data-driven decision-making are expected to overcome these restraints and propel the market toward continued expansion. The market segmentation reveals a strong preference for cloud-based solutions, reflecting the industry's trend toward flexible and scalable IT infrastructure. Large enterprises currently dominate the market share, but SMEs are rapidly adopting data mining software, indicating promising future growth in this segment. Geographic analysis shows that North America and Europe are currently leading the market, but the Asia-Pacific region is poised for significant growth due to increasing digitalization and economic expansion in countries like China and India.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

Title: Identifying Factors that Affect Entrepreneurs’ Use of Data Mining for Analytics Authors: Edward Matthew Dominica, Feylin Wijaya, Andrew Giovanni Winoto, Christian Conference: The 4th International Conference on Electrical, Computer, Communications, and Mechatronics Engineering https://www.iceccme.com/home

This dataset was created to support research focused on understanding the factors influencing entrepreneurs’ adoption of data mining techniques for business analytics. The dataset contains carefully curated data points that reflect entrepreneurial behaviors, decision-making criteria, and the role of data mining in enhancing business insights.

Researchers and practitioners can leverage this dataset to explore patterns, conduct statistical analyses, and build predictive models to gain a deeper understanding of entrepreneurial adoption of data mining.

Intended Use: This dataset is designed for research and academic purposes, especially in the fields of business analytics, entrepreneurship, and data mining. It is suitable for conducting exploratory data analysis, hypothesis testing, and model development.

Citation: If you use this dataset in your research or publication, please cite the paper presented at the ICECCME 2024 conference using the following format: Edward Matthew Dominica, Feylin Wijaya, Andrew Giovanni Winoto, Christian. Identifying Factors that Affect Entrepreneurs’ Use of Data Mining for Analytics. The 4th International Conference on Electrical, Computer, Communications, and Mechatronics Engineering (2024).

Abstract The objective of this work is to improve the quality of the information that belongs to the database CubaCiencia, of the Institute of Scientific and Technological Information. This database has bibliographic information referring to four segments of science and is the main database of the Library Management System. The applied methodology was based on the Decision Trees, the Correlation Matrix, the 3D Scatter Plot, etc., which are techniques used by data mining, for the study of large volumes of information. The results achieved not only made it possible to improve the information in the database, but also provided truly useful patterns in the solution of the proposed objectives.

LScDC Word-Category RIG MatrixApril 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk / suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny MirkesGetting StartedThis file describes the Word-Category RIG Matrix for theLeicester Scientific Corpus (LSC) [1], the procedure to build the matrix and introduces the Leicester Scientific Thesaurus (LScT) with the construction process. The Word-Category RIG Matrix is a 103,998 by 252 matrix, where rows correspond to words of Leicester Scientific Dictionary-Core (LScDC) [2] and columns correspond to 252 Web of Science (WoS) categories [3, 4, 5]. Each entry in the matrix corresponds to a pair (category,word). Its value for the pair shows the Relative Information Gain (RIG) on the belonging of a text from the LSC to the category from observing the word in this text. The CSV file of Word-Category RIG Matrix in the published archive is presented with two additional columns of the sum of RIGs in categories and the maximum of RIGs over categories (last two columns of the matrix). So, the file ‘Word-Category RIG Matrix.csv’ contains a total of 254 columns.This matrix is created to be used in future research on quantifying of meaning in scientific texts under the assumption that words have scientifically specific meanings in subject categories and the meaning can be estimated by information gains from word to categories. LScT (Leicester Scientific Thesaurus) is a scientific thesaurus of English. The thesaurus includes a list of 5,000 words from the LScDC. We consider ordering the words of LScDC by the sum of their RIGs in categories. That is, words are arranged in their informativeness in the scientific corpus LSC. Therefore, meaningfulness of words evaluated by words’ average informativeness in the categories. We have decided to include the most informative 5,000 words in the scientific thesaurus. Words as a Vector of Frequencies in WoS CategoriesEach word of the LScDC is represented as a vector of frequencies in WoS categories. Given the collection of the LSC texts, each entry of the vector consists of the number of texts containing the word in the corresponding category.It is noteworthy that texts in a corpus do not necessarily belong to a single category, as they are likely to correspond to multidisciplinary studies, specifically in a corpus of scientific texts. In other words, categories may not be exclusive. There are 252 WoS categories and a text can be assigned to at least 1 and at most 6 categories in the LSC. Using the binary calculation of frequencies, we introduce the presence of a word in a category. We create a vector of frequencies for each word, where dimensions are categories in the corpus.The collection of vectors, with all words and categories in the entire corpus, can be shown in a table, where each entry corresponds to a pair (word,category). This table is build for the LScDC with 252 WoS categories and presented in published archive with this file. The value of each entry in the table shows how many times a word of LScDC appears in a WoS category. The occurrence of a word in a category is determined by counting the number of the LSC texts containing the word in a category. Words as a Vector of Relative Information Gains Extracted for CategoriesIn this section, we introduce our approach to representation of a word as a vector of relative information gains for categories under the assumption that meaning of a word can be quantified by their information gained for categories.For each category, a function is defined on texts that takes the value 1, if the text belongs to the category, and 0 otherwise. For each word, a function is defined on texts that takes the value 1 if the word belongs to the text, and 0 otherwise. Consider LSC as a probabilistic sample space (the space of equally probable elementary outcomes). For the Boolean random variables, the joint probability distribution, the entropy and information gains are defined.The information gain about the category from the word is the amount of information on the belonging of a text from the LSC to the category from observing the word in the text [6]. We used the Relative Information Gain (RIG) providing a normalised measure of the Information Gain. This provides the ability of comparing information gains for different categories. The calculations of entropy, Information Gains and Relative Information Gains can be found in the README file in the archive published. Given a word, we created a vector where each component of the vector corresponds to a category. Therefore, each word is represented as a vector of relative information gains. It is obvious that the dimension of vector for each word is the number of categories. The set of vectors is used to form the Word-Category RIG Matrix, in which each column corresponds to a category, each row corresponds to a word and each component is the relative information gain from the word to the category. In Word-Category RIG Matrix, a row vector represents the corresponding word as a vector of RIGs in categories. We note that in the matrix, a column vector represents RIGs of all words in an individual category. If we choose an arbitrary category, words can be ordered by their RIGs from the most informative to the least informative for the category. As well as ordering words in each category, words can be ordered by two criteria: sum and maximum of RIGs in categories. The top n words in this list can be considered as the most informative words in the scientific texts. For a given word, the sum and maximum of RIGs are calculated from the Word-Category RIG Matrix.RIGs for each word of LScDC in 252 categories are calculated and vectors of words are formed. We then form the Word-Category RIG Matrix for the LSC. For each word, the sum (S) and maximum (M) of RIGs in categories are calculated and added at the end of the matrix (last two columns of the matrix). The Word-Category RIG Matrix for the LScDC with 252 categories, the sum of RIGs in categories and the maximum of RIGs over categories can be found in the database.Leicester Scientific Thesaurus (LScT)Leicester Scientific Thesaurus (LScT) is a list of 5,000 words form the LScDC [2]. Words of LScDC are sorted in descending order by the sum (S) of RIGs in categories and the top 5,000 words are selected to be included in the LScT. We consider these 5,000 words as the most meaningful words in the scientific corpus. In other words, meaningfulness of words evaluated by words’ average informativeness in the categories and the list of these words are considered as a ‘thesaurus’ for science. The LScT with value of sum can be found as CSV file with the published archive. Published archive contains following files:1) Word_Category_RIG_Matrix.csv: A 103,998 by 254 matrix where columns are 252 WoS categories, the sum (S) and the maximum (M) of RIGs in categories (last two columns of the matrix), and rows are words of LScDC. Each entry in the first 252 columns is RIG from the word to the category. Words are ordered as in the LScDC.2) Word_Category_Frequency_Matrix.csv: A 103,998 by 252 matrix where columns are 252 WoS categories and rows are words of LScDC. Each entry of the matrix is the number of texts containing the word in the corresponding category. Words are ordered as in the LScDC.3) LScT.csv: List of words of LScT with sum (S) values. 4) Text_No_in_Cat.csv: The number of texts in categories. 5) Categories_in_Documents.csv: List of WoS categories for each document of the LSC.6) README.txt: Description of Word-Category RIG Matrix, Word-Category Frequency Matrix and LScT and forming procedures.7) README.pdf (same as 6 in PDF format)References[1] Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2[2] Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v3[3] Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4] WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [5] Suzen, N., Mirkes, E. M., & Gorban, A. N. (2019). LScDC-new large scientific dictionary. arXiv preprint arXiv:1912.06858. [6] Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

Data Mining Tools Market Outlook

According to our latest research, the global Data Mining Tools market size reached USD 1.93 billion in 2024, reflecting robust industry momentum. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 5.69 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics across diverse industries, rapid digital transformation, and the necessity for actionable insights from massive data volumes.

One of the pivotal growth factors propelling the Data Mining Tools market is the exponential rise in data generation, particularly through digital channels, IoT devices, and enterprise applications. Organizations across sectors are leveraging data mining tools to extract meaningful patterns, trends, and correlations from structured and unstructured data. The need for improved decision-making, operational efficiency, and competitive advantage has made data mining an essential component of modern business strategies. Furthermore, advancements in artificial intelligence and machine learning are enhancing the capabilities of these tools, enabling predictive analytics, anomaly detection, and automation of complex analytical tasks, which further fuels market expansion.

Another significant driver is the growing demand for customer-centric solutions in industries such as retail, BFSI, and healthcare. Data mining tools are increasingly being used for customer relationship management, targeted marketing, fraud detection, and risk management. By analyzing customer behavior and preferences, organizations can personalize their offerings, optimize marketing campaigns, and mitigate risks. The integration of data mining tools with cloud platforms and big data technologies has also simplified deployment and scalability, making these solutions accessible to small and medium-sized enterprises (SMEs) as well as large organizations. This democratization of advanced analytics is creating new growth avenues for vendors and service providers.

The regulatory landscape and the increasing emphasis on data privacy and security are also shaping the development and adoption of Data Mining Tools. Compliance with frameworks such as GDPR, HIPAA, and CCPA necessitates robust data governance and transparent analytics processes. Vendors are responding by incorporating features like data masking, encryption, and audit trails into their solutions, thereby enhancing trust and adoption among regulated industries. Additionally, the emergence of industry-specific data mining applications, such as fraud detection in BFSI and predictive diagnostics in healthcare, is expanding the addressable market and fostering innovation.

From a regional perspective, North America currently dominates the Data Mining Tools market owing to the early adoption of advanced analytics, strong presence of leading technology vendors, and high investments in digital transformation. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization, expansion of IT infrastructure, and growing awareness of data-driven decision-making in countries like China, India, and Japan. Europe, with its focus on data privacy and digital innovation, also represents a significant market share, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions modernize their operations and adopt cloud-based analytics solutions.

Component Analysis

The Component segment of the Data Mining Tools market is bifurcated into Software and Services. Software remains the dominant segment, accounting for the majority of the market share in 2024. This dominance is attributed to the continuous evolution of data mining algorithms, the proliferation of user-friendly graphical interfaces, and the integration of advanced analytics capabilities such as machine learning, artificial intelligence, and natural language pro

Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.

This dataset contains the review results of the manuscript of "A Systematic Review on Privacy-Preserving Distributed Data Mining" authored by Chang Sun, Lianne Ippel, Andre Dekker, Michel Dumontier, Johan van Soest. In the datasets, there are 231 published articles about privacy-perserving distributed data mining. Variables include article DOI, title, authors, keywords, user scenarios, distributed data scenarios, privacy/security definition/proof/analysis, privacy statement, privacy-preserving methods category, privacy-preserving methods (specific), data mining problem, data mining/machine learning methods, experiment data information, accuracy of the methods, efficiency (computation and communication cost), and scalability. The search method and evaluation criteria are described in the paper "A Systematic Review on Privacy-Preserving Distributed Data Mining". The DOI and link to the paper will be provided when the paper gets published.

Yearly citation counts for the publication titled "Spatial analysis and data mining techniques for identifying risk factors of Out-of-Hospital Cardiac Arrest".

Yearly citation counts for the publication titled "An improvement of a data mining technique for early detection of at-risk learners in distance learning environments".

Vision-Based ADAS Data Mining Market Outlook

As per our latest research, the global Vision-Based ADAS Data Mining market size stood at USD 2.84 billion in 2024 and is anticipated to reach USD 12.67 billion by 2033, growing at a robust CAGR of 17.8% during the forecast period. The primary growth factor driving this market is the rapid adoption of advanced driver assistance systems (ADAS) in modern vehicles, propelled by stringent safety regulations and the surging demand for enhanced driving experiences globally.

One of the most significant growth drivers for the Vision-Based ADAS Data Mining market is the increasing focus on vehicular safety and the reduction of road accidents. Governments worldwide are imposing stricter mandates on automotive OEMs to integrate advanced safety features such as lane departure warning, traffic sign recognition, and collision avoidance in all new vehicles. The integration of vision-based ADAS, powered by sophisticated data mining techniques, is enabling real-time analysis of driving environments, which significantly reduces the likelihood of human error and enhances overall road safety. As a result, automakers are investing heavily in research and development to improve the accuracy and reliability of these systems, further fueling market expansion.

Another pivotal factor contributing to market growth is the rapid technological advancements in machine learning, deep learning, and computer vision. These technologies are at the core of vision-based ADAS data mining, enabling systems to process vast amounts of visual data from cameras and sensors in real-time. The evolution of high-performance hardware and the proliferation of cloud-based analytics platforms have empowered ADAS solutions to become more intelligent, adaptive, and scalable. This technological leap has made it feasible to deploy sophisticated data mining algorithms even in cost-sensitive vehicle segments, accelerating the democratization of advanced safety features across a broader range of vehicles.

Additionally, the growing consumer inclination towards connected and autonomous vehicles is acting as a catalyst for the Vision-Based ADAS Data Mining market. As automotive manufacturers race to develop next-generation vehicles with semi-autonomous and autonomous capabilities, the demand for robust data mining solutions that can interpret complex traffic scenarios and driver behaviors is escalating. The ability of vision-based ADAS systems to seamlessly integrate with other in-vehicle technologies, such as infotainment and telematics, is further enhancing their value proposition. This convergence is not only improving the overall driving experience but also opening up new avenues for data-driven services and applications within the automotive ecosystem.

From a regional perspective, Asia Pacific is emerging as the most dynamic market for Vision-Based ADAS Data Mining, driven by the rapid expansion of the automotive sector in countries like China, Japan, and South Korea. The region's strong manufacturing base, coupled with supportive government initiatives aimed at promoting vehicle safety, is fostering a fertile environment for the adoption of advanced ADAS technologies. North America and Europe, on the other hand, continue to lead in terms of technological innovation and regulatory enforcement, ensuring that these markets remain at the forefront of global market share and revenue generation.

Component Analysis

The Vision-Based ADAS Data Mining market is segmented by component into hardware, software, and services, each playing a distinct yet interconnected role in enabling advanced driver assistance functionalities. Hardware components such as cameras, sensors, and onboard processing units form the backbone of vision-based ADAS systems. These hardware elements are responsible for capturing and relaying vast amounts of visual and environmental data, which are subsequently processed and analyzed to generate actionable insights for drivers. The continuous evolution of high-resolution cameras and advanced sensor technologies is enhancing the precision and reliability of data collection, thereby improving the overall effectiveness of ADAS solutions.

Software is the core enabler of data mining within vision-based ADAS systems. This segment includes sophisticated algorithms for image processing, pattern recognition, and machine learning, which interpret raw sensor data to id

Abstract Civil structures are usually prone to damage during their service life and it leads them to loss their serviceability and safety. Thus, damage assessment can guarantee the integrity of structures. As a result, a structural damage detection approach including two main components, a set of accelerometers to record the response data and a data mining (DM) procedure, is widely used to extract the information on the structural health condition. In the last decades, DM has provided numerous solutions to structural health monitoring (SHM) problems as an all-inclusive technique due to its powerful computational ability. This paper presents the first attempt to illustrate the data mining techniques (DMTs) applications in SHM through an intensive review of those articles dealing with the use of DMTs aimed for classification-, prediction- and optimization-based data mining methods. According to this categorization, applications of DMTs with respect to SHM research area are classified and it is concluded that, applications of DMTs in the SHM domain have increasingly been implemented, in the last decade and the most popular techniques in the area were artificial neural network (ANN), principal component analysis (PCA) and genetic algorithm (GA), respectively.