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Communications of the ACM, 2000
Phenomenal data mining finds relations between the data and the phenomena that give rise to data rather than just relations among the data..Science and common sense both tell us that the facts about the world are not directly observable but can be inferred from observations about the effects of actions.
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Phenomenal data mining finds relations between the data and the phenomena that give rise to data rather than just relations among the data..Science and common sense both tell us that the facts about the world are not directly observable but can be inferred from observations about the effects of actions.
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Statistics in Medicine, 2003
AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
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AbstractData mining strategies are usually applied to opportunistically collected data and frequently focus on the discovery of structure such as clusters, bumps, trends, periodicities, associations and correlations, quantization and granularity, and other structures for which a visual data analysis is very appropriate and quite likely to yield insight.
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Statistics, Data Mining, and Machine Learning in Astronomy
, 2019Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark ...
Ž. Ivezić +3 more
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Data Mining and Knowledge Discovery in Databases
Advances in Computer and Electrical Engineering, 2019The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods.
A. Azevedo
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ACM SIGMOD Record, 2002
1. What's It All About? 2. Input: Concepts, Instances, Attributes 3. Output: Knowledge Representation 4. Algorithms: The Basic Methods 5. Credibility: Evaluating What's Been Learned 6. Implementations: Real Machine Learning Schemes 7. Moving On: Engineering The Input And Output 8. Nuts And Bolts: Machine Learning Algorithms In Java 9.
Ian H. Witten, Eibe Frank
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1. What's It All About? 2. Input: Concepts, Instances, Attributes 3. Output: Knowledge Representation 4. Algorithms: The Basic Methods 5. Credibility: Evaluating What's Been Learned 6. Implementations: Real Machine Learning Schemes 7. Moving On: Engineering The Input And Output 8. Nuts And Bolts: Machine Learning Algorithms In Java 9.
Ian H. Witten, Eibe Frank
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2013
The trajectories of a moving object are a powerful summary for its activity related to mobility. As seen in previous chapters, such information can be queried in order to retrieve those trajectories (and the objects that own them) that respond to some given search criteria, for instance following a predefined interesting behavior. However, when massive
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The trajectories of a moving object are a powerful summary for its activity related to mobility. As seen in previous chapters, such information can be queried in order to retrieve those trajectories (and the objects that own them) that respond to some given search criteria, for instance following a predefined interesting behavior. However, when massive
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Biomedical Instrumentation & Technology, 2009
Bernard V. Liengme, David J. Ellert
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Bernard V. Liengme, David J. Ellert
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2018
Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an ...
Birant, Derya +2 more
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Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an ...
Birant, Derya +2 more
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Machine learning and data mining in manufacturing
Expert systems with applications, 2021Alican Dogan, Derya Birant
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