As applications generate massive amounts of data streams, the requirement for ways to analyze and cluster this data has become a critical field of research for knowledge discovery. Data stream clustering’s primary objective and goal are to acqu...
Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must ove...
This paper proposes a new evolving algorithm named Macro SOStream with entirely online learning and based on self-organizing density for data stream clustering. The Macro SOStream is based on the SOStream algorithm, but we incorporate macroclusters c...
This paper introduces a new multiclass classifier called the evolving Fuzzy Classifier (eFC). Starting its knowledge base from scratch, the eFC structure evolves based on a clustering algorithm that can add, merge, delete, or update clusters (= rules...
In this comprehensive literature review, we rigorously adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for our process and reporting. This review employs an innovative method integrating the advanc...
Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept d...
The Internet of Things (IoT) uses networked devices, dispersed sensors, and cameras to create huge, diverse data streams. Concept drift, in which the underlying data distribution shifts over time, is frequently caused by the non-stationary and multim...
Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall sh...
The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing...
Evolving fuzzy neural networks have the adaptive capacity to solve complex problems by interpreting them. This is due to the fact that this type of approach provides valuable insights that facilitate understanding the behavior of the problem being an...
The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high...
Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an...
Anomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continu...
Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed...
The proliferation of algorithmically generated malicious URLs has overwhelmed traditional threat intelligence systems, necessitating a paradigm shift from reactive, single-instance analysis to proactive, automated campaign discovery. Existing systems...
Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return g...
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Tidal streams are complex watercourses that represent a transitional zone between riverine and marine systems; they occur where fresh and marine waters converge. Because tidal circulation processes cause substantial turbulence in these highly dynamic...
In domains such as fraud detection, healthcare, and industrial equipment maintenance, streaming data often exhibit characteristics such as continuous generation, high real-time processing requirements, and complex distributions, making it susceptible...
Data prefetching is essential for modern file storage systems operating in large-scale cloud and data-intensive environments, where high performance increasingly depends on intelligent, adaptive mechanisms. Traditional rule-based methods and recently...
Ubiquitous automated data collection at an unprecedented scale is making available streaming, real-time information flows in a wide variety of settings, transforming both science and industry. Learning algorithms deployed in such contexts often rely...
Precision agriculture has increasingly adopted controlled agricultural microclimates, particularly smart greenhouses, as a strategy to enhance crop yields while maintaining environmental conditions within suitable ranges for each crop. Among the vari...
Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networ...
Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability. In this paper, we propose Anomaly Detection with Drift-Aware Ensemble-based Incremental Learning (AD...
Radar systems are mainly used for tracking aircraft, missiles, satellites, and watercraft. In many cases, information regarding the objects detected by a radar system is sent to, and used by, a peripheral consuming system, such as a missile system or...
A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for diff...
Convolutional neural networks (CNNs) are becoming an increasingly popular approach for classification mapping of large complex regions where manual data collection is too time consuming. Stream boundaries in hyper-arid polar regions such as the McMur...
Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the dist...
Continuous healthcare monitoring systems generate non-stationary physiological data streams, where evolving statistical properties and patterns often invalidate static models and fixed user classifications. To address this challenge, we propose drift...
There is an increasing impetus for the use of digital city models and sensor network data to understand the current demand for utility resources and inform future infrastructure service planning across a range of spatial scales. Achieving this requir...
A novel image-reconstruction method is proposed for the processing of data acquired at random spatial positions. The images are reconstructed and updated in real time concurrently with the measurements to produce an evolving image, the quality of whi...
The rise in online transactions for purchasing goods and services can benefit the parties involved. However, it also creates uncertainty and the possibility of fraud-related threats. This work aims to explore and extract knowledge of auction fraud by...
Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Has...
In the event of a crisis, such as COVID-19, the decisions and subsequent actions taken by the local government are one of the primary sources of support to the local population. Yet the processes through which these decisions are reached and the data...
Cloud Manufacturing enables the integration of geographically distributed manufacturing resources through advanced Cloud Computing and IoT technologies. This paradigm promotes the development of scalable and adaptable production systems. However, exi...
This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe di...
Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of pr...
Fault diagnosis of rolling bearings is crucial for ensuring the maintenance and reliability of industrial equipment. Existing cross-domain diagnostic methods often struggle to maintain performance under evolving mechanical and environmental condition...
Genome enabled research has led to a large and ever-growing body of data on Pseudomonas syringae genome variation and characteristics, though systematic capture of this information to maximize access by the research community remains a significant ch...
Data stream classification is a critical challenge in data mining, where models must rapidly adapt to evolving data distributions and concept drift in real time, while extreme learning machines offer fast training and strong generalization, most exis...
In the competitive telecommunications market, the information that the mobile telecom operators can obtain by regularly analysing their massive stored call logs, is of great interest. Although the data that can be extracted nowadays from mobile phone...
As the concept of the Metaverse evolves, virtual reality (VR) plays a pivotal role in creating immersive, socially interactive environments that form the backbone of this interconnected digital universe. However, VR technology often faces significant...
In the rapidly evolving landscape of scientific semiconductor laboratories (commonly known as, cleanrooms), integrated with Internet of Things (IoT) technology and Cyber-Physical Systems (CPSs), several factors including operational changes, sensor a...
Manufacturing companies strive to minimize costs, maximize efficiency and improve production quality, which is crucial for market competitiveness. As companies grow and technologies evolve, increasingly complex challenges arise in effectively managin...
Currently, the increase in devices capable of continuously collecting data on non-stationary and dynamic variables affects predictive models, particularly if they are not equipped with algorithms capable of adapting their parameters and structure, ca...
This review examines the evolving intersections of data analytics, machine learning, and artificial intelligence—terms that have been frequently conflated since 2016 during a period of increased hype and investment. Following recent reviews acr...
Test-time adaptation (TTA) enhances model performance in target domains by dynamically adjusting parameters using unlabeled test data. However, existing TTA methods typically assume balanced data distributions, whereas real-world test data is often i...
Takagi–Sugeno–Kang (TSK) fuzzy classifiers have achieved notable success in pattern recognition and data mining due to their combination of approximation capability and model interpretability. However, existing TSK classifiers predominant...
The problem of flow control under conditions of a turbulent boundary layer at transonic and supersonic free-stream velocities is considered. Such flows are integral components of the flight process and exert significant effects on the flow around bot...
This research advances the study of learning under non-stationary conditions by proposing a unified framework for concept drift detection and adaptive regression in evolving data streams. Unlike traditional batch models that assume static data distri...
The development of a resilient and circular bio-based economy is of paramount importance, notably in the EU, where current climate policies and evolving regulations strongly demand more sustainable practices, impacting monitoring and reporting, as we...
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