[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119442-en":3,"doc-seo-119442-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119442,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","EMPIRICAL EVALUATION IN DATA MINING IN ASSOCIATION WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING","Data analysis and decision-making have been transformed by modern data mining developments powered by Artificial Intelligence, Machine Learning, and Deep Learning. AI- and ML-enhanced methods strengthen and extend traditional data mining as the volume and complexity of information continue to grow. The paper reviews current research trends, highlighting Auto-ML, Federated Learning for privacy-preserving mining, and Explainable AI (XAI) to improve transparency. It also discusses multimodal and graph-based mining, real-time edge analytics, and ethics-driven work on bias reduction, justice, and privacy. Future research is expected to tackle explainability and privacy while handling more complex, unstructured data, supporting emerging directions in information mining with AI and ML.","N  \n20(2): S2: 62-67, 2025  \n[www.thebioscan.com](www.thebioscan.com)  \nEMPIRICAL EVALUATION IN DATA MINING IN ASSOCIATION WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING  \nR Priyanka, Research Scholar  \nDepartment of computer science, AJK College of Arts and Science [Email Address: ](Email Address: priyankaramalingam25@gmail.com)[priyankaramalingam25@gmail.com](Email Address: priyankaramalingam25@gmail.com)  \nDr. B. Suresh Kumar, Associate Professor  \nDepartment of computer science, AJK college of arts and science  \nEmail Address: [sureshkumar@ajkcas.com](sureshkumar@ajkcas.com)  \n[DOI: 10.63001/tbs.2025.v20.i02.S2.pp62-67](DOI: 10.63001/tbs.2025.v20.i02.S2.pp62-67)  \nKEYWORDS  \nData Mining and Data Warehousing, Big Data,  \nDeep Learning, Machine Learning, Artificial Intelligence  \nReceived on:  \n12-02-2025  \nAccepted on:  \n15-03-2025 Published on:  \n17-04-2025  \nABSTRACT  \nData analysis and decision-making have undergone a dramatic revolutionize as a consequence of modern improvements in data mining, which are driven by Artificial Intelligence, Machine Learning, and Deep Learning. Artificial Intelligence and Machine Learning approaches are increasingly being used to supplement and improve traditional data mining techniques asthe dimensions and involvedness of information persist to increase. With an emphasis on advancements generated by AI and ML, this article examines the most recent research trends in data mining. The usage of Auto-ML to automate machine learning processes, Federated Learning for privacy-preserving data mining, and Explainable AI (XAI) to increase model transparency are important areas of study. More dynamic and scalable solutions are also being made possible by the combination of multimodal data mining, graph-based algorithms, and real-time analytics at the edge. In response to ethical concerns about AI-driven decision-making systems, efforts to reduce bias, maintain justice, and protect privacy have also gained traction. More precise, scalable, and effective solutions are becoming possible in a variety of segments, such as healthiness care, economics, and security, thanks to the convergence of Artificial Intelligence, Machine Learning, along with data mining. Future studies will probably concentrate on resolving issues with explainability, privacy, and processing more complicated, unstructured data as these technologies develop further. This research paper provides an indication of these budding trends and discusses the impending directions for expectations research in the sector of information mining in perspective of AI and ML.  \nINTRODUCTION  \nFinding patterns, connections, and valuable information in massive databases via computational methods is known as data mining. It entails taking raw data and turning it into intelligible structures by identifying hidden, previously undiscovered patterns. When combined with ML furthermore AI, information mining becomes an effective technique for drawing insightful conclusions from vast volumes of data. AI and ML improve data mining skills and automate the discovery process.  \nKey Concepts in Data Mining with AI and ML including:  \n• Data Preprocessing: The data must be cleansed, converted, and arranged before any machine learning or artificial intelligence algorithms are applied. In order to improve model performance, this stage may involve addressing missing values, eliminating outliers, normalizing data, and feature selection.  \n• Data Exploration: Data scientists investigate the dataset at this point in order to comprehend its distribution, structure, and interrelationships. Data analysis and the selection of suitable modeling strategies are frequently guided by statistical tools and visualizations.  \n• Organized Learning: A sort of ML called organized learning utilizes marked as data—that is, data with known results—to train a model. The algorithm uses input-output pairs to learn how to predict or categorize. Typical algorithms for supervised learning incl","cbCaiaiFnXHTEsdF","https://ap.wps.com/l/cbCaiaiFnXHTEsdF","pdf",398146,1,6,"English","en",105,"# Introduction\n## Key Concepts in Data Mining with AI and ML\n### Data Preprocessing\n### Data Exploration\n### Supervised Learning\n### Unsupervised Learning\n### Reinforcement Learning\n### Deep Learning\n### Evaluation and Model Selection","[{\"question\":\"What is data mining and how does AI/ML enhance it?\",\"answer\":\"Data mining uses computational methods to discover patterns, connections, and valuable information from large databases. When combined with ML and AI, it supports converting raw data into understandable structures and improves or automates the discovery of insights.\"},{\"question\":\"Which supervised learning approaches are commonly mentioned?\",\"answer\":\"The paper notes linear regression, decision trees, random forests, logistic regression for binary classification, and support vector machines (SVM) for classification and regression tasks.\"},{\"question\":\"How do federated learning and Explainable AI (XAI) contribute to modern data mining?\",\"answer\":\"Federated Learning enables privacy-preserving data mining by reducing direct exposure of data, while XAI increases model transparency to address concerns about AI-driven decision-making.\"}]","EMPIRICAL EVALUATION IN DATA MINING IN ASSOCIATION WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING | PDF",1785724303,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"empirical-evaluation-in-data-mining-in-association-with-artificial-intelligence-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/empirical-evaluation-in-data-mining-in-association-with-artificial-intelligence-and-machine-learning/119442/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is data mining and how does AI/ML enhance it?","Question",{"text":75,"@type":76},"Data mining uses computational methods to discover patterns, connections, and valuable information from large databases. When combined with ML and AI, it supports converting raw data into understandable structures and improves or automates the discovery of insights.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which supervised learning approaches are commonly mentioned?",{"text":80,"@type":76},"The paper notes linear regression, decision trees, random forests, logistic regression for binary classification, and support vector machines (SVM) for classification and regression tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"How do federated learning and Explainable AI (XAI) contribute to modern data mining?",{"text":84,"@type":76},"Federated Learning enables privacy-preserving data mining by reducing direct exposure of data, while XAI increases model transparency to address concerns about AI-driven decision-making.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]