{"product_id":"visual-knowledge-discovery-and-machine-learning-reversible-n-d","title":"Visual Knowledge Discovery and Machine Learning - Reversible n-D","description":"\u003cp\u003eIn this review of Visual Knowledge Discovery and Machine Learning the focus is on readers who work with complex, high-dimensional datasets and need visualization techniques that do not sacrifice original information. The book introduces the General Lines Coordinates (GLCs), a class of reversible lossless 2-D and 3-D visualization methods, and pairs those representations with algorithms for classification, clustering, dimension reduction, and Pareto optimization. This review explains why practitioners in data science, researchers in machine learning, and advanced students will find the book most useful and what limitations to expect.\u003c\/p\u003e\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eGeneral Lines Coordinates:\u003c\/strong\u003e Presents reversible, lossless 2-D and 3-D visual representations that preserve n-D information for faithful exploration of complex patterns.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eAlgorithms for n-D tasks:\u003c\/strong\u003e Supplies a coherent set of algorithms for classification and clustering tailored to benefit from the GLC visual framework.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eDimension reduction and optimization:\u003c\/strong\u003e Explains methods for dimension reduction and Pareto optimization that integrate with visualization to support decision making on trade-offs.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTheoretical foundations:\u003c\/strong\u003e Includes mathematical and theoretical analysis that underpins the GLC methodology and justifies its claimed properties.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCase studies:\u003c\/strong\u003e Demonstrates the approach on real-world examples, including the Challenger disaster analysis, world hunger data, health monitoring, image processing, and text classification.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eInterdisciplinary scope:\u003c\/strong\u003e Bridges visualization and machine learning to offer tools useful across data science, AI research, and applied analytics.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eData scientists, machine learning researchers, and graduate students who routinely handle high-dimensional datasets will benefit most from the book because it delivers both new visual representations and accompanying algorithms that preserve original n-D information. Practitioners who need interpretable visual methods that can be inverted back to the source dimensions will find the GLC approach especially valuable.\u003c\/p\u003e\n\u003cp\u003eThose seeking a beginner introduction to standard machine learning or a cookbook of drop-in software tools should look elsewhere, as the book emphasizes theoretical foundations, new methodology, and case study demonstrations rather than step-by-step software tutorials or beginner-level explanations.\u003c\/p\u003e\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduces a novel reversible visualization family that preserves full n-D information for accurate analysis.\u003c\/li\u003e\n\u003cli\u003eCombines visualization with concrete algorithms for classification, clustering, and dimension reduction in a unified framework.\u003c\/li\u003e\n\u003cli\u003eSupports claims with mathematical analysis and diverse case studies that demonstrate practical value across domains.\u003c\/li\u003e\n\u003cli\u003eUseful for interpreting complex trade-offs via integrated Pareto optimization techniques.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eFocused on theory and methodology rather than providing ready-made software, which may limit immediate hands-on adoption for some users.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eVisual Knowledge Discovery and Machine Learning\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAuthor\u003c\/td\u003e\n\u003ctd\u003eBoris Kovalerchuk\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMain topic\u003c\/td\u003e\n\u003ctd\u003eReversible n-D visualization and machine learning methods\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eKey method\u003c\/td\u003e\n\u003ctd\u003eGeneral Lines Coordinates (GLCs)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIncluded content\u003c\/td\u003e\n\u003ctd\u003eAlgorithms, mathematical analysis, case studies\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eExample applications\u003c\/td\u003e\n\u003ctd\u003eChallenger disaster, world hunger, health monitoring, image and text tasks\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eVisual Knowledge Discovery and Machine Learning is a thoughtful, method-focused work that advances reversible visualization for high-dimensional data and couples it with algorithms that leverage those views. Researchers and advanced practitioners who need faithful, interpretable mappings between n-D datasets and 2-D\/3-D displays will find strong methodological value here, though those expecting a plug-and-play software manual may need to supplement it with implementation work.\u003c\/p\u003e\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes the book include software code?\u003c\/strong\u003e\u003cbr\u003eThe book focuses on methodology, algorithms, and theoretical analysis; it demonstrates methods with case studies but does not emphasize packaged software.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is the central visualization approach?\u003c\/strong\u003e\u003cbr\u003eThe central approach is General Lines Coordinates (GLCs), a class of reversible, lossless 2-D and 3-D visual representations for n-D data.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWho benefits most from these methods?\u003c\/strong\u003e\u003cbr\u003eResearchers, advanced data scientists, and graduate students handling complex, high-dimensional datasets will benefit most from the GLC framework and associated algorithms.\u003c\/p\u003e","brand":"Boris Kovalerchuk","offers":[{"title":"Default Title","offer_id":48145701961947,"sku":"3319892304","price":174.83,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/61tt_DvjAVL._SL1254.jpg?v=1768735313","url":"https:\/\/gearmusthave.com\/products\/visual-knowledge-discovery-and-machine-learning-reversible-n-d","provider":"GearMustHave","version":"1.0","type":"link"}