Results 261 to 270 of about 459,037 (316)
Transcriptomics Data Mining to Identify Novel Regulatory Genes of Iron Uptake in Drought-Stressed Wheat. [PDF]
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Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001
Data mining started its move out of the statistics and machine learning ghettos and into the mainstream almost 10 years ago. With great fanfare and a large influx of venture capital, data mining was going to change the very nature of business. Yet data mining products have had relatively modest success in the marketplace.
Syam Menon, Ramesh Sharda
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Data mining started its move out of the statistics and machine learning ghettos and into the mainstream almost 10 years ago. With great fanfare and a large influx of venture capital, data mining was going to change the very nature of business. Yet data mining products have had relatively modest success in the marketplace.
Syam Menon, Ramesh Sharda
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Genetic Epidemiology, 2005
Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of
L Adrienne, Cupples +8 more
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Group 14 used data-mining strategies to evaluate a number of issues, including appropriate diagnosis, haplotype estimation, genetic linkage and association studies, and type I error. Methods ranged from exploratory analyses, to machine learning strategies (neural networks, supervised learning, and tree-based methods), to false discovery rate control of
L Adrienne, Cupples +8 more
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From data mining to wisdom mining
Journal of Information Science, 2021The knowledge gained from data mining is highly dependent on the experience of an expert for further analysis to increase effectiveness and wise decision-making. This mined knowledge requires actionability enhancement before it can be applied to real-world problems. The literature highlights the reasons that emerged the need to incorporate human wisdom
Salma Khan, Muhammad Shaheen
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2015 International Conference on Computing, Networking and Communications (ICNC), 2015
Data compression plays an important role in data mining in assessing the minability of data and a modality of evaluating similarities between complex objects. We focus on compressibility of strings of symbols and on using compression in computing similarity in text corpora; also we propose a novel approach for assessing the quality of text ...
Dan A. Simovici +3 more
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Data compression plays an important role in data mining in assessing the minability of data and a modality of evaluating similarities between complex objects. We focus on compressibility of strings of symbols and on using compression in computing similarity in text corpora; also we propose a novel approach for assessing the quality of text ...
Dan A. Simovici +3 more
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Data Mining mit Prozessdaten (Data Mining with Process Data)
at - Automatisierungstechnik, 2005Abstract Durch die zunehmende Komplexität der Produktionsprozesse in innovativen Anwendungsfeldern wird die Prozessanalyse und -Modellierung vor neue Herausforderungen gestellt. Typische Problemfelder sind reaktive Feststoffprozesse, komplexe Polymerisationsprozesse und insbesondere biotechnologische Prozesse.
Andreas Schuppert, Rainer Perne
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On Multi-Relational Data Mining for Foundation of Data Mining
2007 IEEE/ACS International Conference on Computer Systems and Applications, 2007Multi-relational data mining (MRDM) deals with knowledge discovery from relational databases consisting of one or multiple tables. As a typical technique for MRDM, inductive logic programming (ILP) has the power of dealing with reasoning related to various data mining tasks in a "unified" way.
Miao Liu, Hai-Feng Guo, Zhengxin Chen
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IEEE Transactions on Knowledge and Data Engineering, 2014
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences.
Xindong Wu 0001 +3 more
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Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences.
Xindong Wu 0001 +3 more
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FROM DATA MINING TO BEHAVIOR MINING [PDF]
International Journal of Information Technology & Decision Making, 2006 Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of ...
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2019
We are living in a world of information abundance, surplus, and access. We have technologies to acquire any type of information but we still face the challenge of extracting the underlying valuable knowledge. Data analyses and mining processes may be severely impaired whenever data are corrupted by noise, ambiguity and distortions.
Alexandra Oliveira +4 more
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We are living in a world of information abundance, surplus, and access. We have technologies to acquire any type of information but we still face the challenge of extracting the underlying valuable knowledge. Data analyses and mining processes may be severely impaired whenever data are corrupted by noise, ambiguity and distortions.
Alexandra Oliveira +4 more
openaire +2 more sources