Longbing Cao – författare
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Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
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2 036 kr
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1 123 kr
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1 123 kr
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1 459 kr
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1 060 kr
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1 408 kr
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1 060 kr
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549 kr
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687 kr
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549 kr
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1 681 kr
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1 123 kr
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2 490 kr
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844 kr
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Behavior and Social Computing
International Workshop on Behavior and Social Informatics, BSI 2013, Gold Coast, Australia, April 14-17, and International Workshop on Behavior and Social Informatics and Computing, BSIC 2013, Beijing, China, August 3-9, 2013, Revised Selected Papers
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Agents and Data Mining Interaction
10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers
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This book constitutes the thoroughly refereed and revised selected papers from the 10th International Workshop on Agents and Data Mining Interactions, ADMI 2014, held in Paris, France, in May 2014 as satellite workshop of AAMAS 2014, the 13th International Conference on Autonomous Agents and Multiagent Systems.
The 11 papers presented were carefully reviewed and selected from numerous submissions for inclusion in this volume. They present current research and engineering results, as well as potential challenges and prospects encountered in the respective communities and the coupling between agents and data mining.
Advances in Knowledge Discovery and Data Mining
21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part I
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The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
Advances in Knowledge Discovery and Data Mining
21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, 2017, Proceedings, Part II
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The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
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This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists?
Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.
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