USING CLASSIFICATION ON UPISI 2011 DATABASE
Date Issued
2012
Author(s)
Kjiroski, Kiril
Madevska Bogdanova, Ana
Abstract
Data mining is the process of applying these methods to data
with the intention of uncovering hidden patterns. [1]
Classification is one of the most common techniques of data
mining, which occurs very frequently in everyday life.
Classification is the central data mining technique that we use
in this research. Since classification involves diving up
objects so that each of these objects will fall into one of
mutually exhaustive and exclusive categories we call classes,
we use this technique to classify the numbers of enrolment of
a student needed to complete a certain course. In this paper,
we will be using some of the most frequently used
classification methods. These methods will be tested on a
chosen dataset to serve as an example of which of these
methods is most suitable for such dataset form. The dataset is
extracted from Application “Upisi”, and treated with three
classification approaches: Naive Bayes, Nearest Neighbour
and Decision Trees. Our main goal is to compare the results
gain from the database of the application Upisi in 2010 [2]
and the results from 2011. Through this comparison we
establish how to improve our classification technique.
with the intention of uncovering hidden patterns. [1]
Classification is one of the most common techniques of data
mining, which occurs very frequently in everyday life.
Classification is the central data mining technique that we use
in this research. Since classification involves diving up
objects so that each of these objects will fall into one of
mutually exhaustive and exclusive categories we call classes,
we use this technique to classify the numbers of enrolment of
a student needed to complete a certain course. In this paper,
we will be using some of the most frequently used
classification methods. These methods will be tested on a
chosen dataset to serve as an example of which of these
methods is most suitable for such dataset form. The dataset is
extracted from Application “Upisi”, and treated with three
classification approaches: Naive Bayes, Nearest Neighbour
and Decision Trees. Our main goal is to compare the results
gain from the database of the application Upisi in 2010 [2]
and the results from 2011. Through this comparison we
establish how to improve our classification technique.
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