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Performance Evaluation in Education System Using SLIQ Decision Tree Classification Algorithm.

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Title: Performance Evaluation in Education System Using SLIQ Decision Tree Classification Algorithm.
Authors: Gill, Amit Kumar, Pahwa, Jaya, Kumar, Lokesh
Source: International Journal of Computer Science & Management Studies; Aug2014, Vol. 14 Issue 8, p1-4, 4p
Subject Terms: Network performance, Education, Decision trees, Algorithms, Information retrieval, Information theory
Abstract: At the present time, the amount of data stored in educational database is increasing rapidly. These databases contain hidden information for improvement of student's performance. Decision tree is the most useful classification algorithm in educational data mining because of its ease of execution and easier to understand compared to other algorithms. The ID3, C4.5 and CART decision tree algorithms has been applied on the data of students to predict their performance. But all these 3 algorithms are used only for small database. For large database, we are using a new algorithm i.e. SLIQ which removes all the memory restriction and accuracy problem comes in other algorithms. It is fast and scalable than others because it can be implemented in both serial and parallel fashion for good data placement and load balancing. In this paper, I would like to implement SLIQ decision tree algorithm and then compare its results with other algorithms like Random forest and Random tree to find out which gives the better performance for predicting the improvement in performance of students. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computer Science & Management Studies is the property of Imperial Foundation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Complementary Index
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  Label: Title
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  Data: Performance Evaluation in Education System Using SLIQ Decision Tree Classification Algorithm.
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  Data: <searchLink fieldCode="AR" term="%22Gill%2C+Amit+Kumar%22">Gill, Amit Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Pahwa%2C+Jaya%22">Pahwa, Jaya</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Lokesh%22">Kumar, Lokesh</searchLink>
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  Data: International Journal of Computer Science & Management Studies; Aug2014, Vol. 14 Issue 8, p1-4, 4p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Network+performance%22">Network performance</searchLink><br /><searchLink fieldCode="DE" term="%22Education%22">Education</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Information+theory%22">Information theory</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: At the present time, the amount of data stored in educational database is increasing rapidly. These databases contain hidden information for improvement of student's performance. Decision tree is the most useful classification algorithm in educational data mining because of its ease of execution and easier to understand compared to other algorithms. The ID3, C4.5 and CART decision tree algorithms has been applied on the data of students to predict their performance. But all these 3 algorithms are used only for small database. For large database, we are using a new algorithm i.e. SLIQ which removes all the memory restriction and accuracy problem comes in other algorithms. It is fast and scalable than others because it can be implemented in both serial and parallel fashion for good data placement and load balancing. In this paper, I would like to implement SLIQ decision tree algorithm and then compare its results with other algorithms like Random forest and Random tree to find out which gives the better performance for predicting the improvement in performance of students. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Computer Science & Management Studies is the property of Imperial Foundation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        Type: general
      – SubjectFull: Education
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      – SubjectFull: Decision trees
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      – SubjectFull: Algorithms
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      – SubjectFull: Information retrieval
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      – SubjectFull: Information theory
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      – TitleFull: Performance Evaluation in Education System Using SLIQ Decision Tree Classification Algorithm.
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            NameFull: Gill, Amit Kumar
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              Text: Aug2014
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              Y: 2014
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