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RISS 인기검색어
- 검색키워드
- 저자 : Irfan Ajmal Khan (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Irfan Ajmal Khan,Junghyun Woo,Ji-Hoon Seo,Jin-Tak Choi 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.11
Text mining is a specific method to extract knowledge from structured and unstructured data. This extracted knowledge from text mining process can be used for further usage and discovery. This paper presents the method for extraction information from unstructured text data and the importance of Association Rules Mining, specifically for of Korean language (text) and also, NLP (Natural Language Processing) tools are explained. Association Rules Mining (ARM) can also be used for mining association between itemsets from unstructured data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining method calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data, select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.
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An Application of Educational Data Mining (EDM) Technique for Scholarship Prediction
Irfan Ajmal Khan,Jin Tak Choi 보안공학연구지원센터 2014 International Journal of Software Engineering and Vol.8 No.12
Mining data in educational filed is an important and useful task for anybody related to educational institute. The useful information mined from the data can help with performance, guidance, teaching, planning and etc. for staff, students and instructors. Different educational data mining researches has been carried out on student data which includes the student’s basic achievements and educational background, academic scores and the amount of credit hours, but the relations between these are limited. Therefore in this paper we have described a system using data mining technologies such as decision tree. We have analyzed ID3 and J48 (C4.5) algorithms for predicting the scholarship winning chances on student data by translating the decision tree into “IF-THEN” rules and implementing these rules for prediction in our system called scholarship calculator. We have mined student data to calculate the chances of winning scholarship depending on their semester grades, position/rank of student in class, achievements, maximum and minimum amount of taken and allowed credit hours and extra curriculum activities. We found that ID3 works better even though J48 is faster in classifying data and creates a smaller tree than ID3. Because of ID3’s bigger tree it has more rule, more rules means more crosschecking and deeper decision, that’s why the predicted result was more accurate than J48. We have also described how our scholarship calculator works to predict and calculate the chances of winning scholarship. Performance evaluation is done and the results are also compared with already existing datasets. The developed system could be very useful in predicting student’s chances of winning scholarship from the first semester. It can help students to pinpoint the weak areas, which can be perfected with proper guidance from instructors and staff for better chances of winning scholarship.
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