Comparative Analysis of Various Data Mining Techniques on Educational Datasets
Full text
Figure
Related documents
The main techniques for data mining include classification and prediction, clustering, outlier detection, association rules, sequence analysis, time series analysis and text mining,
the same price as the VAT-inclusive price of comparable new housing, but the resale of the used property is not subject to the VAT, the owner of used housing should be able to sell
Protein-polysaccharide conjugates prepared via Maillard reactions can enhance the colloidal stability properties in O/W emulsions, compared to native proteins especially
To have a good understanding of the effects of temperature and humidity on feed intake, we fit linear mixed effect models using the summary statistics.. The summary statistics
There are several major data mining techniques have been developed and used in data mining projects recently including association, classification, clustering, prediction
There are several major data mining techniques have been developed and used in data mining projects recently including association, classification, clustering,
A binary logit model was employed for farmers’ participation in conservation agriculture shows education level, number of active family labour and main employment of farmers
If data mining techniques such as clustering, decision tree, association, classification and prediction can be applied to higher education processes, it can