Results 301 to 310 of about 23,785,135 (371)
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A survey on federated learning in data mining
WIREs Data Mining Knowl. Discov., 2021Data mining is a process to extract unknown, hidden, and potentially useful information from data. But the problem of data island makes it arduous for people to collect and analyze scattered data, and there is also a privacy security issue when mining ...
Bin Yu +4 more
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Review Paper Data Mining Klasifikasi Data Mining
Jurnal Ilmiah Informatika Global, 2023The process of combining statistical techniques, mathematical calculations, Artificial Intelligence (AI) and machine learning to extract useful and interrelated information from large amounts of data. Data mining is commonly used to analyze and explore big data to get useful information.
Srirahayu, Agustina +1 more
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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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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
semanticscholar +1 more source
Clinics in Laboratory Medicine, 2008
Large-scale clinical databases provide a detailed perspective on patient phenotype in disease and the characteristics of health care processes. Important information is often contained in the relationships between the values and timestamps of sequences of clinical data.
Andrew R, Post, James H, Harrison
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Large-scale clinical databases provide a detailed perspective on patient phenotype in disease and the characteristics of health care processes. Important information is often contained in the relationships between the values and timestamps of sequences of clinical data.
Andrew R, Post, James H, Harrison
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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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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
semanticscholar +1 more source
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.
Oliveira, Alexandra +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.
Oliveira, Alexandra +4 more
openaire +2 more sources
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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2008
After the introduction and development of the relational database model between 1970 and the 1980s, this model proved to be insufficiently expressive for specific applications dealing with, for instance, temporal data, spatial data and multi-media data.
Kuijpers B +4 more
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After the introduction and development of the relational database model between 1970 and the 1980s, this model proved to be insufficiently expressive for specific applications dealing with, for instance, temporal data, spatial data and multi-media data.
Kuijpers B +4 more
openaire +3 more sources