Research on naive bayes by using rapid miner device
By: Widiharto
.
Contributor(s): Soeleman, M. Arief.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.24(1), Jan-Feb.Description: 12-14p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: Data mining is a procedure used to evaluate and find the hidden knowledge of a database. It is applicable in
various sectors such as weather forecast, hospitals, business industries and many more. Currently, education
data mining is the most useful application used to analyze data related to student performance, course outlines,
and faculty performance. This paper describes different classification techniques by using large and small
datasets. These two datasets are dataset examples used through repository sites. Several instances depend upon
these sites. These data sets are applied on Naive Bayes type to show that it is the first-class classifier from small
and big statistics sets. This paper offers the observe and evaluation of numerous methodologies used for
prediction. Based on observation, Naïve Bayes is more appropriate for small datasets based at the assessment
executed on this paper the use of numerous methodologies pushed through the RapidMiner device whilst
equating precision, consider and accuracy.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2072 |
Data mining is a procedure used to evaluate and find the hidden knowledge of a database. It is applicable in
various sectors such as weather forecast, hospitals, business industries and many more. Currently, education
data mining is the most useful application used to analyze data related to student performance, course outlines,
and faculty performance. This paper describes different classification techniques by using large and small
datasets. These two datasets are dataset examples used through repository sites. Several instances depend upon
these sites. These data sets are applied on Naive Bayes type to show that it is the first-class classifier from small
and big statistics sets. This paper offers the observe and evaluation of numerous methodologies used for
prediction. Based on observation, Naïve Bayes is more appropriate for small datasets based at the assessment
executed on this paper the use of numerous methodologies pushed through the RapidMiner device whilst
equating precision, consider and accuracy.
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