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emphasis is placed on the use of data mining concepts in real world applications with large database components. KEY TOPICS: Includes unique;
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, scholars, professionals, students, and academicians. Selected topics include: Real-World Data Mining Applications, Challenges, and;
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Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI;
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outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies;
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tutorials. Real-World Data Mining will be valuable to professionals on analytics teams; professionals seeking certification in the field;
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* Create models that solve real-world problems * Design and develop data mining applications using a variety of datasets * Perform object;
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Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The;
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Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The;
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uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45;
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in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object;
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A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and;
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Data mining applications range from commercial to social domains, with novel applications appearing swiftly; for example, within the;
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of strategic decision-making Emphasizes the use of data mining concepts in real-world scenarios with large database components Focuses on data;
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This book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data;
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Graph data is powerful, thanks to its ability to model arbitrary relationship between objects and is encountered in a range of real-world;
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mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this;
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requirements of mobile data mining applications: energy-saving, personalization, and real-time Energy saving is a fundamental requirement;
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of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio;
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of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio;
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Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples;
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Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a do-it-yourself approach;
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studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud;
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This book discusses the challenges facing current research in knowledge discovery and data mining posed by the huge volumes of complex data;
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Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies;
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Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies;
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Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that;
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mining for emerging applications, including adaptable malware detection, insider threat detection, firewall policy analysis, and real-time data;
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