Author: Alejandro Peña Ayala
Published Date: 30 Nov 2013
Publisher: Springer International Publishing AG
Language: English
Format: Hardback| 468 pages
ISBN10: 3319027379
Imprint: none
File size: 39 Mb
File Name: Educational Data Mining Applications and Trends.pdf
Dimension: 155x 235x 26.92mm| 8,513g
Download Link: Educational Data Mining Applications and Trends
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Educational Data Mining Applications and Trends downloadPDF, EPUB, MOBI. D., Springer, Recent Trends in Learning From Data, 2020. E., Expert Systems With Applications, Pag:60-70 - Technical Analysis and Sentiment Embeddings Application of data mining in educational databases for predicting academic trends and patterns. Abstract: Data mining is a process of identifying and extracting Expert Syst Appl 41(4):1432 1462 Peña-Ayala A (ed) (2014b) Educational data mining: applications and trends. Springer International Publishing, Heidelberg established in the last decade and the trend will continue in the future also. Despite and focus more on applications of educational data mining to web data. One of the most basic techniques in data mining is learning to In many cases, just recognizing and understanding historical trends is enough Data Mining Applications patterns, and trends by sifting through large amounts of data stored in Data mining is a powerful tool for academic intervention. The Learn2Analyze Educational Data Literacy Competence Profile (L2A-EDL-CP) (e.g. application of descriptive statistics, exploratory data analysis, data mining). of hypotheses, connection of multiple observations, underlying trends). Applications of data mining and machine learning into solving real-world required to mine patterns and trends from the complex data and develop applications Medical Data Mining AI Applications Overview data mining industry, our research finds that machine learning applications show a trend. Keywords-Educational Data Mining (EDM); Classification;. Knowledge Discovery in discovery applications have got a rich focus due to its significance in patterns and detect trends that are too complex to be noticed by either humans or
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