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Synopsis
This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic–based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems.
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Text Mining using Big Data (Paperback)
Published by
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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Paperback. Condition: new. Paperback. This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic-based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9786209795305
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Text Mining using Big Data
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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Softcover
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Text Mining using Big Data
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LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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PAP
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9786209795305
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Text Mining using Big Data
Published by
LAP LAMBERT Academic Publishing Mär 2026, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 156 pp. Englisch. Seller Inventory # 9786209795305
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Text Mining using Big Data
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic-based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems. Seller Inventory # 9786209795305
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Text Mining using Big Data
Published by
LAP LAMBERT Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
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Taschenbuch. Condition: Neu. Text Mining using Big Data | Rafeeq Ahmed (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209795305 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Seller Inventory # 134944643
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Text Mining using Big Data
Published by
LAP LAMBERT Academic Publishing Mär 2026, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic-based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. Seller Inventory # 9786209795305
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Text Mining using Big Data (Paperback)
Published by
LAP Lambert Academic Publishing, 2026
ISBN 10: 6209795307
ISBN 13: 9786209795305
New
Paperback
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. This book presents a comprehensive study on text mining using big data analytics with a novel fuzzy logic-based approach. As the volume of unstructured textual data grows exponentially across digital platforms, extracting meaningful knowledge from large-scale datasets has become a significant challenge. The book introduces advanced methodologies that integrate big data frameworks with fuzzy set theory to address uncertainty, ambiguity, and vagueness inherent in textual information. It explores preprocessing techniques, feature extraction, semantic analysis, and scalable mining strategies designed for high-dimensional data environments. A fuzzy inference model is proposed to enhance classification, clustering, and decision-making accuracy in complex text datasets. Experimental evaluations demonstrate improved performance compared to traditional crisp-based models. This work serves as a valuable resource for researchers, academicians, and practitioners working in data science, artificial intelligence, and big data analytics, providing both theoretical foundations and practical implementations for intelligent text mining systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9786209795305
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