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Educational data mining and learning analytics: An updated survey
WIREs Data Mining Knowl. Discov., 2020This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have ...
C. Romero, S. Ventura
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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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Fake News Detection on Social Media: A Data Mining Perspective
SKDD, 2017Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media.
Kai Shu +4 more
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Encyclopedia of Education and Information Technologies, 2020
Problem difficulty estimates play important roles in a wide variety of educational systems, including determining the sequence of problems presented to students and the interpretation of the resulting responses.
R. Baker +20 more
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Problem difficulty estimates play important roles in a wide variety of educational systems, including determining the sequence of problems presented to students and the interpretation of the resulting responses.
R. Baker +20 more
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Data Mining for the Social Sciences, 2019
Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and signiicant structures, from large amounts of data stored in databases, data warehouses, or other information repositories.
Kimberly Kirkpatrick
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Data mining is the process of discovering interesting knowledge, such as patterns, associations, changes, anomalies and signiicant structures, from large amounts of data stored in databases, data warehouses, or other information repositories.
Kimberly Kirkpatrick
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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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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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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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2005
Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions,
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Decision makers thirst for answers to questions. As more data is gathered, more questions are posed: Which customers are most likely to respond positively to a marketing campaign, product price change or new product offering? How will the competition react? Which loan applicants are most likely or least likely to default? The ability to raise questions,
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International Journal of Knowledge Discovery in Bioinformatics, 2009
In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds.
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In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds.
openaire +3 more sources