Results 271 to 280 of about 459,037 (316)
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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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Proceedings of the 24th International Conference on World Wide Web, 2015
The fairly recent explosion in the availability of reasonably fast wireless and mobile data networks has spurred demand for more capable mobile computing devices. Conversely, the emergence of new devices increases demand for better networks, creating a virtuous cycle.
Spiros Papadimitriou, Tina Eliassi-Rad
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The fairly recent explosion in the availability of reasonably fast wireless and mobile data networks has spurred demand for more capable mobile computing devices. Conversely, the emergence of new devices increases demand for better networks, creating a virtuous cycle.
Spiros Papadimitriou, Tina Eliassi-Rad
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2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2018
The paper focuses on identifying patterns in medical data that can bring economic advantage to medical facilities. The main objective is to determine which algorithm fits best on a medical data set in order to determine how likely a patient will return for a new consult.
Bogdan Nedelcu +2 more
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The paper focuses on identifying patterns in medical data that can bring economic advantage to medical facilities. The main objective is to determine which algorithm fits best on a medical data set in order to determine how likely a patient will return for a new consult.
Bogdan Nedelcu +2 more
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2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007
Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of software development processes and the final software product. Number of researches who employ such techniques and methods on software cost and effort estimation are increasing. This
Burak Turhan, F. Onur Kutlubay
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Data mining techniques and machine learning methods are commonly used in several disciplines. It is possible that they could also provide a basis for quality assessment of software development processes and the final software product. Number of researches who employ such techniques and methods on software cost and effort estimation are increasing. This
Burak Turhan, F. Onur Kutlubay
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Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr), 2002
The paper reports a preliminary investigation of the use of of modern data mining tools for mortgage scoring. Using IBM's Intelligent Miner (a data mining toolbox), the authors built a model of serious delinquency on a sample of data from Mortgage Information Corporation's Loan Performance System, which contains over 20 million loans with a volume of ...
George H. John, Yin Zhao
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The paper reports a preliminary investigation of the use of of modern data mining tools for mortgage scoring. Using IBM's Intelligent Miner (a data mining toolbox), the authors built a model of serious delinquency on a sample of data from Mortgage Information Corporation's Loan Performance System, which contains over 20 million loans with a volume of ...
George H. John, Yin Zhao
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IEEE Software, 2013
Last century, it wasn't known if data miners could find structure within software projects. This century, we know better: data mining has been successfully applied to many different artifacts from software projects. So it's time to move on to "What's next?" In the author's view, "discussion mining" is the next great challenge for the predictive ...
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Last century, it wasn't known if data miners could find structure within software projects. This century, we know better: data mining has been successfully applied to many different artifacts from software projects. So it's time to move on to "What's next?" In the author's view, "discussion mining" is the next great challenge for the predictive ...
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2011
Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems.
Shampa Chakraverty +3 more
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Data mining algorithms are widely used today for the analysis of large corporate and scientific datasets stored in databases and data archives. Industry, science and commerce fields often need to analyze very large datasets maintained over geographically distributed sites by using the computational power of distributed and parallel systems.
Shampa Chakraverty +3 more
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Data mining and electroencephalography
Statistical Methods in Medical Research, 2000An overview of data mining (DM) and its application to the analysis of DM and electroencephalography (EEG) is given by: (i) presenting a working definition of DM, (ii) motivating why EEG analysis is a challenging field of application for DM technology and (iii) by reviewing exemplary work on DM applied to EEG analysis.
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2019
This article presents a broad overview of the main clustering methodologies. It is accomplished by introducing the clustering problem and the key elements characterizing it. In particular, we describe different distance and similarity measures which can be used in a clustering method.
Amelio A., Tagarelli A.
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This article presents a broad overview of the main clustering methodologies. It is accomplished by introducing the clustering problem and the key elements characterizing it. In particular, we describe different distance and similarity measures which can be used in a clustering method.
Amelio A., Tagarelli A.
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Innovation and future of mining rock mechanics
Journal of Rock Mechanics and Geotechnical Engineering, 2021Q Wang
exaly