WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Dimensionality Reduction for Data Analysis With Quantum Feature Learning","authors":"Shyam R. Sihare","doi":"10.1002/widm.1568","DOIUrl":"https://doi.org/10.1002/widm.1568","url":null,"abstract":"To improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we investigate several qDR techniques on a variety of datasets such as quantum Gaussian distribution adaptation (qGDA), quantum principal component analysis (qPCA), quantum linear discriminant analysis (qLDA), and quantum t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, and Iris are among the datasets used in this investigation. Through comparison evaluations against well‐established classical approaches, such as classical PCA (cPCA), classical LDA (cLDA), and classical GDA (cGDA), and using well‐established metrics like loss, fidelity, and processing time, the effectiveness of these techniques is assessed. The findings show that cPCA produced positive results with the lowest loss and highest fidelity when used on the Iris dataset. On the other hand, quantum uniform manifold approximation and projection (qUMAP) performs well and shows strong fidelity when tested against the Wine dataset, but ct‐SNE shows mediocre performance against the Digits dataset. Isomap and locally linear embedding (LLE) function differently depending on the dataset. Notably, LLE showed the largest loss and lowest fidelity on the Olivetti Faces dataset. The hypothesis testing findings showed that the qDR strategies did not significantly outperform the classical techniques in terms of maintaining pertinent information from quantum datasets. More specifically, the outcomes of paired <jats:italic>t</jats:italic>‐tests show that when it comes to the ability to capture complex patterns, there are no statistically significant differences between the cPCA and qPCA, the cLDA and qLDA, and the cGDA and qGDA. According to the findings of the assessments of mutual information (MI) and clustering accuracy, qPCA may be able to recognize patterns more clearly than standardized cPCA. Nevertheless, there is no discernible improvement between the qLDA and qGDA approaches and their classical counterparts.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678437","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":"Business Analytics in Customer Lifetime Value: An Overview Analysis","authors":"Onur Dogan, Abdulkadir Hiziroglu, Ali Pisirgen, Omer Faruk Seymen","doi":"10.1002/widm.1571","DOIUrl":"https://doi.org/10.1002/widm.1571","url":null,"abstract":"In customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied to gain deeper insights into customer behaviors and decision‐making processes. Despite the recognized importance of CLV, there is a noticeable gap in comprehensive analyses and reviews of BA techniques applied to CLV. This study aims to fill this gap by conducting a thorough survey of the state‐of‐the‐art investigations on CLV models integrated with BA approaches, thereby contributing to a research agenda in this field. The review methodology consists of three main steps: identification of relevant studies, creating a coding plan, and ensuring coding reliability. First, relevant studies were identified using predefined keywords. Next, a coding plan—one of the study's significant contributions—was developed to evaluate these studies comprehensively. Finally, the coding plan's reliability was tested by three experts before being applied to the selected studies. Additionally, specific evaluation criteria in the coding plan were implemented to introduce new insights. This study presents exciting and valuable results from various perspectives, providing a crucial reference for academic researchers and marketing practitioners interested in the intersection of BA and CLV.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594489","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":"Knowledge Graph for Solubility Big Data: Construction and Applications","authors":"Xiao Haiyang, Yan Ruomei, Wu Yan, Guan Lixin, Li Mengshan","doi":"10.1002/widm.1570","DOIUrl":"https://doi.org/10.1002/widm.1570","url":null,"abstract":"Dissolution refers to the process in which solvent molecules and solute molecules attract and combine with each other. The extensive solubility data generated from the dissolution of various compounds under different conditions, is distributed across structured or semi‐structured formats in various media, such as text, web pages, tables, images, and databases. These data exhibit multi‐source and unstructured features, aligning with the typical 5 V characteristics of big data. A solubility big data technology system has emerged under the fusion of solubility data and big data technologies. However, the acquisition, fusion, storage, representation, and utilization of solubility big data are encountering new challenges. Knowledge Graphs, known as extensive systems for representing and applying knowledge, can effectively describe entities, concepts, and relations across diverse domains. The construction of solubility big data knowledge graph holds substantial value in the retrieval, analysis, utilization, and visualization of solubility knowledge. Throwing out a brick to attract a jade, this paper focuses on the solubility big data knowledge graph and, firstly, summarizes the architecture of solubility knowledge graph construction. Secondly, the key technologies such as knowledge extraction, knowledge fusion, and knowledge reasoning of solubility big data are emphasized, along with summarizing the common machine learning methods in knowledge graph construction. Furthermore, this paper explores application scenarios, such as knowledge question answering and recommender systems for solubility big data. Finally, it presents a prospective view of the shortcomings, challenges, and future directions related to the construction of solubility big data knowledge graph. This article proposes the research direction of solubility big data knowledge graph, which can provide technical references for constructing a solubility knowledge graph. At the same time, it serves as a comprehensive medium for describing data, resources, and their applications across diverse fields such as chemistry, materials, biology, energy, medicine, and so on. It further aids in knowledge retrieval and mining, analysis and utilization, and visualization across various disciplines.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563113","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":"Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis","authors":"S. Tamilselvan, G. Dhanalakshmi, D. Balaji, L. Rajeshkumar","doi":"10.1002/widm.1564","DOIUrl":"https://doi.org/10.1002/widm.1564","url":null,"abstract":"Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition processing and the information supply renders versatile and affordable technological system. Besides, the accuracy with which the soft computing technique predicts the parameters has transformed the industrial productivity to a whole new level. The interest of this article focuses on versatile applications of SC methods to forecast the technological changes which intend to reorient the progress of various industries, and this is ascertained by a patent landscape analysis. The patent landscape revealed the players who are in the segment consistently and this also provides how this field moves on in the future and who could be a dominant country for a specific technology. Alongside, the accuracy of the soft computing method for a particular practice has also been mentioned indicating the feasibility of the technique. The novel part of this article lies in patent landscape analysis compared with the other data while the other part is the discussion of application of computational techniques to various industrial practices. The progress of various engineering applications integrating them with the patent landscape analysis must be envisaged for a better understanding of the future of all these applications resulting in an improved productivity.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561881","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":"Using Machine Learning for Systematic Literature Review Case in Point: Agile Software Development","authors":"Itzik David, Roy Gelbard","doi":"10.1002/widm.1569","DOIUrl":"https://doi.org/10.1002/widm.1569","url":null,"abstract":"Systematic literature reviews (SLRs) are essential for researchers to keep up with past and recent research in their domains. However, the rapid growth in knowledge creation and the rising number of publications have made this task increasingly complex and challenging. Moreover, most systematic literature reviews are performed manually, which requires significant effort and creates potential bias. The risk of bias is particularly relevant in the data synthesis task, where researchers interpret each study's evidence and summarize the results. This study uses an experimental approach to explore using machine learning (ML) techniques in the SLR process. Specifically, this study replicates a study that manually performed sentiment analysis for the <jats:italic>data synthesis</jats:italic> step to determine the polarity (negative or positive) of evidence extracted from studies in the field of agile methodology. This study employs a lexicon‐based approach to sentiment analysis and achieves an accuracy rate of approximately 86.5% in identifying study evidence polarity.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"237 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536805","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":"Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans","authors":"Jon Vadillo, Roberto Santana, Jose A. Lozano","doi":"10.1002/widm.1567","DOIUrl":"https://doi.org/10.1002/widm.1567","url":null,"abstract":"Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out‐of‐distribution inputs. In this paper, we comprehensively review the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios where a human assesses not only the input and the output classification, but also the explanation of the model's decision. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment. Based on this framework, we provide a structured review of the diverse attack paradigms existing in this domain, identify current gaps and future research directions, and illustrate the main attack paradigms discussed. Furthermore, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536806","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":"Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023)","authors":"Tanvir Habib Sardar","doi":"10.1002/widm.1566","DOIUrl":"https://doi.org/10.1002/widm.1566","url":null,"abstract":"The traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based partitioning, hierarchical, and density clustering algorithms over the past decade (2013–2023). A digital text‐mining‐based comprehensive search technique with multiple field‐specific keywords, inclusion measures, and exclusion criteria is employed to obtain the research landscape from the Scopus database. The Scopus‐obtained data is analyzed using the VOSViewer software tool and coded using the R statistical analysis tool. The analysis identifies the numbers of scholarly articles, diversities of article sources, their impact and growth patterns, details of most influential authors and co‐authors, most cited articles, most contributing affiliations and countries, and their collaborations, use of different keywords and their impact, and so forth. The study further explores the articles and reports the methodologies employed for designing MapReduce‐based counterparts of traditional partitioning, hierarchical, and density clustering algorithms and their optimizations and hybridizations. Finally, the study lists the main research challenges encountered in the past decade for MapReduce‐based partitioning, hierarchical, and density clustering. It suggests possible areas for future research to contribute further in this field.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142486813","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 Conceptual Framework for Human‐Centric and Semantics‐Based Explainable Event Detection","authors":"Taiwo Kolajo, Olawande Daramola","doi":"10.1002/widm.1565","DOIUrl":"https://doi.org/10.1002/widm.1565","url":null,"abstract":"Explainability in the field of event detection is a new emerging research area. For practitioners and users alike, explainability is essential to ensuring that models are widely adopted and trusted. Several research efforts have focused on the efficacy and efficiency of event detection. However, a human‐centric explanation approach to existing event detection solutions is still lacking. This paper presents an overview of a conceptual framework for human‐centric semantic‐based explainable event detection with the acronym HUSEED. The framework considered the affordances of XAI and semantics technologies for human‐comprehensible explanations of events to facilitate 5W1H explanations (Who did what, when, where, why, and how). Providing this kind of explanation will lead to trustworthy, unambiguous, and transparent event detection models with a higher possibility of uptake by users in various domains of application. We illustrated the applicability of the proposed framework by using two use cases involving first story detection and fake news detection.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448763","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":"An overview of current developments and methods for identifying diabetic foot ulcers: A survey","authors":"L. Jani Anbarasi, Malathy Jawahar, R. Beulah Jayakumari, Modigari Narendra, Vinayakumar Ravi, R. Neeraja","doi":"10.1002/widm.1562","DOIUrl":"https://doi.org/10.1002/widm.1562","url":null,"abstract":"Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Application Areas &gt; Health Care</jats:list-item> <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":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397864","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":"Multimodal emotion recognition: A comprehensive review, trends, and challenges","authors":"Manju Priya Arthanarisamy Ramaswamy, Suja Palaniswamy","doi":"10.1002/widm.1563","DOIUrl":"https://doi.org/10.1002/widm.1563","url":null,"abstract":"Automatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers from 2017 to 2023 to reflect on the current trends in multimodal affective computing. This article covers various modalities used in emotion recognition based on the emotional response system under four categories: subjective experience comprising text and self‐report; peripheral physiology comprising electrodermal, cardiovascular, facial muscle, and respiration activity; central physiology comprising EEG, neuroimaging, and EOG; behavior comprising facial, vocal, whole‐body behavior, and observer ratings. This review summarizes the measures and behavior of each modality under various emotional states. This article provides an extensive list of multimodal datasets and their unique characteristics. The recent advances in multimodal emotion recognition are grouped based on the research focus areas such as emotion elicitation strategy, data collection and handling, the impact of culture and modality on multimodal emotion recognition systems, feature extraction, feature selection, alignment of signals across the modalities, and fusion strategies. The recent multimodal fusion strategies are detailed in this article, as extracting shared representations of different modalities, removing redundant features from different modalities, and learning critical features from each modality are crucial for multimodal emotion recognition. This article summarizes the strengths and weaknesses of multimodal emotion recognition based on the review outcome, along with challenges and future work in multimodal emotion recognition. This article aims to serve as a lucid introduction, covering all aspects of multimodal emotion recognition for novices.This article is categorized under:<jats:list list-type=\"simple\"> <jats:list-item>Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction</jats:list-item> <jats:list-item>Technologies &gt; Cognitive Computing</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":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397863","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}