WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Decoding Multimodal Text Analytics: Tasks, Datasets, Fusion Models, and Future Frontiers","authors":"Tanusree Nath, Vedika Gupta, Manjari Gupta, Rajesh Sharma","doi":"10.1002/widm.70083","DOIUrl":"https://doi.org/10.1002/widm.70083","url":null,"abstract":"It is estimated that the volume of data on the digital fronts will grow exponentially to reach a volume of 180 zettabytes by 2025, and more than 90% of this data will be of unstructured forms. The unimodal to multimodal text analytics (MTA) has been triggered by this phenomenon. The early introduction of the multimodal text were observed in scholarly literature and industrial use-cases during the early 2010s. Since then, it has greatly expanded its horizons in other sectors such as healthcare, e-commerce, education and public safety. This survey presents a task-oriented, modality-inclusive, and dataset-aware synthesis of recent advancements in MTA, which offers an in-depth review of 10 core text analytics tasks through a multimodal lens. We systematically analyze over 160 research studies and categorize more than 120 state-of-the-art models, spanning fusion strategies, representation learning, transformer architectures, and pretrained vision-language frameworks (e.g., CLIP, ViLBERT). In a variety of datasets including CMU-MOSI, CMU-MOSEI, IEMOCAP, and MAViT-Bangla, multimodal models achieve up to 18%–25% <i>F</i>1-score improvements over text-only baselines, captured in the standardized task-wise comparison tables that are part of this survey. Moreover, this survey discusses seven under-explored tasks, including personality detection, satire detection, and author profiling, and elaborates gaps in research in modality fusion, diversity of data sets, and social inclusivity in these tasks. It does not only fill gaps in the current literature by unifying knowledge in different fields, but also offers researchers working on MTA a future path. It is the first survey that puts all the key tasks within multimodal text analytics into a contiguous and consistent overview compared to other surveys that either refer to multimodal computing at an administrative level or concentrate on a specific task.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147626019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Pattern-Based Datasets for Cyber Attack Detection Using Machine-Learning Techniques","authors":"Pedro Díaz García, Diego López Abril, Javier Bermejo Higuera, Víctor Gonzalez Llorente, Miguel Angel Sicilia Urbán","doi":"10.1002/widm.70081","DOIUrl":"https://doi.org/10.1002/widm.70081","url":null,"abstract":"This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on machine learning. A comparative study of different proposals will be carried out to detect shortcomings and areas for improvement in this field, which will serve as a starting point for future work. To this end, search and quality criteria have been defined to select a suitable set of works to be reviewed. A detailed analysis of the publications under study reveals the heterogeneity of criteria when it comes to research on attack pattern datasets. The variety of datasets analyzed, as well as the disparity in the selection of metrics and classification algorithms, makes it very difficult to establish clear comparative criteria. Further research is needed in this area to have updated datasets that include a catalogue of attack patterns as broad as possible. It is also worth highlighting the convenience of using new techniques and algorithms for the identification of attack patterns, such as those related to Deep Learning.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Transfer Learning Research From the Perspective of Knowledge Graph: Visual Analysis Based on the Bibliometric Method","authors":"Yiquan An, Nuo Hou, Zhimin Lv","doi":"10.1002/widm.70084","DOIUrl":"https://doi.org/10.1002/widm.70084","url":null,"abstract":"As a novel paradigm to overcome the problems of limited data and model generalization in the era of big data, transfer learning (TL) methods have become an essential branch of machine learning. However, the academic community still needs a data‐driven, dynamic, and quantitatively structured review of TL. This paper visually summarizes the literature growth and associated disciplinary trends of TL topics from a knowledge mapping perspective using 4496 documents collected in the WOS core collection, combined with bibliometric. The most essential 30 TL research results were identified and mapped spatial–temporally. This was followed by a structured analysis of the most influential research vectors including countries, institutions, authors, and publication sources. Following this, the knowledge base, core topic distribution, hot topics, and knowledge evolution process of TL research are systematically analyzed around the construction of a co‐cited literature network and keyword co‐occurrence network. The challenges of TL research are thereby summarized and future directions are proposed. This study provides researchers in the area of TL with a holistic insight into research trends, as well as conclusive and comprehensive analysis results that may help relevant scholars better grasp its dynamic direction. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147598833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Epidemiology: The Next Frontier in Precision Public Health","authors":"Suriyaraj Shanmugam Prema, Dhanraj Ganapathy, Deepankumar Shanmugamprema","doi":"10.1002/widm.70077","DOIUrl":"https://doi.org/10.1002/widm.70077","url":null,"abstract":"Artificial intelligence (AI) is revolutionizing the field of epidemiology, offering transformative capabilities in disease surveillance, predictive analytics, and precision public health. By harnessing vast datasets from electronic health records, genomic sequences, and real-time mobility data, AI-driven algorithms enable early detection of outbreaks, accurate disease modeling, and personalized intervention strategies. This paper explores the multifaceted applications of AI in epidemiology, highlighting its role in enhancing disease prediction, exposure assessment, and outcome classification. AI-powered platforms have demonstrated their efficacy in identifying emerging infectious threats, including the early detection of COVID-19 outbreaks. Additionally, AI-driven simulations provide policymakers with actionable insights, optimizing resource allocation and evaluating the effectiveness of public health interventions. However, challenges related to data privacy, algorithmic bias, and model transparency remain significant barriers to widespread adoption. This paper emphasizes the need for robust regulatory frameworks and interdisciplinary collaboration to ensure the responsible deployment of AI technologies in public health. By balancing innovation with ethical considerations, AI has the potential to drive a new era of precision epidemiology, transforming how diseases are monitored, prevented, and controlled on a global scale.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Bibliometric Analysis of Process Mining","authors":"Seyfullah Tokumaci, Onur Dogan, Oguzhan Ozturk","doi":"10.1002/widm.70080","DOIUrl":"https://doi.org/10.1002/widm.70080","url":null,"abstract":"Process mining (PM) has emerged as a pivotal discipline in data science, bridging traditional process analysis with data‐driven techniques to extract actionable insights from event logs. This study conducts a comprehensive bibliometric analysis of 1764 peer‐reviewed articles from the Web of Science database to map the conceptual structure, trends, and future directions of PM research. Employing citation and co‐word analyses, the research identifies influential studies, dominant themes, and emerging topics. Findings reveal that PM literature is receiving heightened academic focus while increasingly integrating artificial intelligence (AI) and machine learning (ML) techniques. Key influential works focus on foundational algorithms (e.g., Alpha Miner, Heuristic Miner) and practical applications in sectors such as healthcare and finance. The analysis highlights the PM's expanding interdisciplinary reach, particularly in the fields of healthcare and education. This study provides a systematic evaluation of PM's evolution, offering a roadmap for researchers to advance theoretical foundations and practical implementations in this rapidly evolving field. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Application Areas &gt; Business and Industry </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147523975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Forgetting to Future: A Survey of Machine Unlearning Approaches","authors":"Sheetal Sehgal, Ankita Verma, Himani Bansal","doi":"10.1002/widm.70082","DOIUrl":"https://doi.org/10.1002/widm.70082","url":null,"abstract":"The Right to Erasure has facilitated the erasure of data as part of ethical and legal compliance for all users. In this regard, Machine Unlearning (MUL) is an emerging tool that transforms the existing trained model to execute comprehensive data erasure. Although beneficial to a few, the rapid development of unlearning algorithms has hindered a beginner's ability to recognize the relationship between the algorithm's productivity and model effectiveness. Though machine unlearning can be quite instrumental in building ethical and trustworthy AI, it is not beyond corrections. Substantive gaps in the form of standardization, formal guarantees, and verifiable implementation do exist. The aim of this paper, therefore, is to present a comprehensive understanding of the machine unlearning field literature and practices. Over 100 peer‐reviewed articles were reviewed, available from 2019 to 2025, including those on federated learning, continual learning, graphical neural networks, and rapidly growing models like generative and large language models. The paper presents a critical analysis of evaluation metrics, unlearning verification, efficiency, model utility on retained data, scalability, and handling of interdependent and multimodal data. Furthermore, it proposes future research directions, pressing the need for effective and efficient unlearning mechanisms. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Evolving Landscape of Modern Regression: A Systematic Review and Mapping Study of Penalized, Algorithmic, and Interpretable Models","authors":"Michael Bendersky, Shimon Fridkin","doi":"10.1002/widm.70078","DOIUrl":"https://doi.org/10.1002/widm.70078","url":null,"abstract":"This review provides a comprehensive Systematic Literature Review (SLR) and Systematic Mapping Study (SMS) of regression research published in Wiley‐indexed venues between <jats:italic>1975</jats:italic> and <jats:italic>2025</jats:italic> . Analyzing a validated corpus of 1466 publications—of which 1174 dated studies form the empirical base—this study traces the theoretical, methodological, and epistemic evolution of regression modeling. The findings reveal a progressive convergence between classical penalized methods (e.g., LASSO, Elastic Net), Bayesian shrinkage approaches, and machine learning‐based regressors such as ensembles and neural networks. From 2022 onward, a distinct interpretability‐driven era emerges, marked by the selective uptake of post hoc explanation techniques like SHAP and LIME. Despite their conceptual appeal, these tools remain underutilized in the corpus—due in part to computational costs, concerns over stability, and the prioritization of embedded interpretability strategies. Through thematic synthesis, the review identifies persistent trade‐offs in regression design—bias versus variance, sparsity versus signal retention, and accuracy versus interpretability—and highlights exemplar hybrid approaches that balance these tensions. The study concludes with a research agenda targeting five frontiers: (1) integrated interpretability systems, (2) unified theoretical modeling frameworks, (3) scalable regression for high‐dimensional and streaming data, (4) robust and uncertainty‐aware modeling, and (5) fairness and transparency in applied settings. Together, these contributions reposition regression as a unified, interdisciplinary field grounded in statistical insight, computational design, and ethical accountability.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward","authors":"Shimon Fridkin, Michael Bendersky","doi":"10.1002/widm.70075","DOIUrl":"https://doi.org/10.1002/widm.70075","url":null,"abstract":"The rapid adoption of complex “black‐box” models in high‐stakes domains has made interpretability a functional prerequisite for artificial intelligence. This systematic literature review synthesizes the state‐of‐the‐art in Interpretable Machine Learning (IML) by analyzing 352 studies retrieved from a multi‐channel digital ecosystem, including Wiley Online Library, IEEE Xplore, ACM Digital Library, and specialized AI repositories such as arXiv and PMLR. To ensure a comprehensive longitudinal perspective, the review covers a 75‐year window (1950–2025), tracing the field from foundational game‐theoretic axioms—specifically the Shapley value (1953)—to contemporary deep‐learning‐driven breakthroughs. We propose a structured taxonomy that categorizes interpretability methods into three distinct families: intrinsic models, post hoc model‐agnostic techniques (including RISE), and deep learning‐specific methods (including Grad‐CAM and its established variants). Our analysis reveals a paradigm shift from passive feature attribution toward “mechanistic interpretability” and actionable recourse. Furthermore, we critically assess emerging evaluation frameworks, highlighting the gap between algorithmic fidelity and human comprehension. This review provides a rigorous evidence base for researchers and practitioners seeking to develop AI systems that are transparent, robust, and aligned with human reasoning.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147478100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for Hate Speech Moderation: A Stakeholder‐Centered and Sociotechnical Review","authors":"Muhammad Deedahwar Mazhar Qureshi, M. Atif Qureshi, Wael Rashwan","doi":"10.1002/widm.70076","DOIUrl":"https://doi.org/10.1002/widm.70076","url":null,"abstract":"The growing use of automated hate speech detection systems has sparked significant concern across technical, ethical, and sociopolitical dimensions. While recent advances in natural language processing and machine learning have improved classification performance, they often do so at the expense of transparency, fairness, and user trust. Explainable artificial intelligence (XAI) offers promising tools to bridge this gap, but current methods remain fragmented in scope and limited in stakeholder alignment. This review synthesizes the state of the art in explainability for hate speech detection by integrating machine learning, human‐computer interaction, and critical social science perspectives. We survey major XAI approaches, including ante‐hoc, post hoc, local, global, counterfactual, and rationale‐based methods, and evaluate their applicability across different stages of the ML pipeline and their relevance to key stakeholder groups such as developers, content moderators, policymakers, and affected communities. To guide this synthesis, we propose a conceptual framework that maps the intersection of explanation strategies, pipeline stages, and stakeholder needs. Using this model, we identify persistent gaps in dataset transparency, cultural and linguistic robustness, explanation evaluation practices, and participatory design processes. We argue that achieving meaningful explainability in content moderation goes beyond technical optimization; it demands sociotechnical alignment, contextual sensitivity, and accountability mechanisms that reflect the lived realities of those impacted by algorithmic decisions. The article concludes with interdisciplinary recommendations focused on dataset development, hybrid evaluation benchmarks, inclusive design, and integration. These strategies aim to foster hate speech detection systems that are not only more explainable but also more just, inclusive, and socially grounded. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Commercial, Legal, and Ethical Issues &gt; Social Considerations </jats:list-item> <jats:list-item> Fundamental Concepts of Data and Knowledge &gt; Explainable AI </jats:list-item> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Gastrointestinal Disease Diagnosis: A Systematic Review of Endoscopy, Histology, and Radiology Applications","authors":"Sameena Pathan, Alen Shahini, Massimo Salvi, Tanweer Ali, Jovita Relasha Lewis, Filippo Molinari, U. Rajendra Acharya","doi":"10.1002/widm.70074","DOIUrl":"https://doi.org/10.1002/widm.70074","url":null,"abstract":"Gastrointestinal (GI) diseases remain among the leading causes of global mortality, with early detection directly linked to survival outcomes. While previous reviews have focused on single imaging modalities, this systematic review uniquely examines artificial intelligence applications across endoscopic, radiological, and histological approaches, reflecting actual clinical diagnostic pathways. This systematic review analyzes 76 high-quality studies (2016–2024) and provides the first comprehensive assessment of how AI performs across different imaging techniques for GI abnormality detection. This multi-modal perspective is particularly timely as healthcare systems move toward integrated diagnostic workflows. Our analysis reveals endoscopy as the most widely used modality (<i>n</i> = 44), particularly for <i>Helicobacter pylori</i>, colorectal polyps, and ulcerative colitis detection. Histological analysis emerges as the second most common approach (<i>n</i> = 25), especially for celiac disease and ulcerative colitis, while CT imaging (<i>n</i> = 10) primarily supports colorectal polyp detection. Deep learning methods significantly outnumber traditional machine learning techniques (68 vs. 8 studies), consistently achieving 90%–99% diagnostic accuracy across multiple disease categories. However, these systems face significant implementation barriers to clinical adoption. Most validation is still conducted in controlled, single-center settings using curated datasets that poorly reflect clinical complexity. Future studies must prioritize multicenter validation, standardized imaging protocols and preprocessing pipelines, and the integration of interpretable AI models capable of providing transparent diagnostic rationale. This review maps the current technical landscape while highlighting critical translational challenges that must be addressed to enable real-world impact.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"99 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147287165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}