Extraction of Dominant Attributes and Guidance Rules for Scholastic Achievement Using Rough Set Theory in Data Mining

Extracting hidden information from a huge set of data is an important and a challenging task in data mining. Data with credibility and relevance plays a vital role in this task. Not to get sidetracked, it is important to ensure that genuine and good quality data is being used. This paper brings out...

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Bibliographic Details
Published inInternational journal of computer science issues Vol. 7; no. 3; p. 28
Main Authors JansiRani, P G, Bhaskaran, R
Format Journal Article
LanguageEnglish
Published Mahebourg International Journal of Computer Science Issues (IJCSI) 01.05.2010
Subjects
Algorithms
Collection
Computer science
Data mining
Education
Pilots
Set theory
Students
Tasks
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Summary:Extracting hidden information from a huge set of data is an important and a challenging task in data mining. Data with credibility and relevance plays a vital role in this task. Not to get sidetracked, it is important to ensure that genuine and good quality data is being used. This paper brings out the significance and highlights the efforts for the collection of genuine and good quality data of the academic performance of students by carrying out many pilot and through studies. with different types of questionnaires. An algorithm, using rough set theory, is developed to implement in the data collected from various college students to identify the dominant attributes to be strengthened. Reducts and guidance rules, using rough set theory in data mining, are computed to facilitate the educators and high and low academic achievers to identify the attributes to be strengthened to have scholastic achievement.
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ISSN:1694-0814
1694-0784