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Gautam, Pratima
- Machine Learning Technique to Predicting Student Performance in Higher Education
Authors
1 Bhopal (M. P.), IN
2 IT Dept., AISECT University, Bhopal (M. P.), IN
Source
Software Engineering, Vol 6, No 8 (2014), Pagination: 236-239Abstract
One of the famous and practical methods for inductive implication over directed data is Decision Tree learning. Decision tree is suitable to classifying categorical data using attributes of database. In this paper educational data mining has been used on qualitative data of students and analysis their performance using c4.5 decision tree algorithm.
The results indicate that student’s performance also influenced by qualitative data. Acquired knowledge in form of tree is easy to assimilate by users.
Keywords
Decision Tree, Learning, Prediction, Qualitative Data.
- A Partition Model for Multilevel Association Rule Mining
Authors
1 Manit, Bhopal, IN
2 Department of Mathematics & Computer Application, Manit, Bhopal, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 8 (2011), Pagination: 507-515Abstract
We have extended the capacity of the learn of mining association rules from single level to multiple concept levels and studied methods for mining multiple-level association rules from large transaction databases. Mining multiple-level association rules may lead to progressive mining of refined knowledge from data and have interesting applications for knowledge discovery in transaction databases, as well as other business or engineering databases. Mining frequent patterns in huge transactional database is an extremely researched area in the field of data mining. Mining frequent itemsets is a basic problem for mining association rules. Taking out association rules at multiple levels helps in discovers more specific and applicable knowledge. Even as computing the number of occurrence of an item we require to scan the given database lots of times. Thus we used partition method and boolean methods for finding frequent itemsets at each concept levels which reduce the number of scans, I/O cost and also reduce CPU overhead. In this paper a new approach is introduced for solving the above mentioned issues. Therefore this algorithm is above all fit for very large size databases. We also use a top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. This method first finds frequent data items at the topmost level and then progressively deepens the mining process into their descendants at lower concept levels.Keywords
Association Rule, Frequent Itemset, Transaction Database, Tree Map, Multilevel Association Rule, Level Wise Filtered Tables.- A Fast Algorithm for Multilevel Association Rule Using Hash Based Method
Authors
1 Maulana Azad National Institute of Technology, Bhopal, IN
2 Department of Mathematics and Computer Application in Maulana Azad National Institute of Technology, Bhopal, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 7 (2010), Pagination: 123-129Abstract
Data mining is having a vital role in many of the applications like market-basket analysis, in biotechnology field etc. In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database. The problem of developing models and algorithms for multilevel association mining pose for new challenges for mathematics and computer science. In most of the studies, multilevel rules will be mined through repeated mining from databases or mining the rules at each individually levels, it affects the efficiency, integrality and accuracy. This paper proposes a hash based method for multilevel association rule mining, which extracting knowledge implicit in transactions database with different support at each level. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. This approach incorporates boundaries instead of sharp boundary intervals. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under different supports in a simple and effective manner.
Keywords
Association Rule, Multilevel, Frequent Items, Transactional Database.- Fuzzy Prediction: An Accurate Approach of Performance Prediction in Present Scenario of Higher Education
Authors
1 Department of Computer Science, BMM, Bhilai, Chhattisgarh, IN
2 Department of IT, Rabindranath Tagore University, Bhopal., IN
Source
Fuzzy Systems, Vol 11, No 3 (2019), Pagination: 45-48Abstract
Due to ease of application and capability to provide accurate and gradual responses, neural networks have become very popular over the recent past when it comes to classification problems. Also, data mining has been used extensively with good effect for decision making in educational system. Improved assessment technique is of paramount importance in understanding, analysing and assessing the progress in performance of the candidates in higher education sectors. Availability of a prediction tool to asses such progression accurately can be boon to organisations. In our work we proposed a model called Fuzzy decision tree model which uses the data of the student to analyse and evaluate their performance. The data include various factors such as previous year results, academic performance, sports interest, social activities etc. to predict their success rate. Use of such model will enable the organisation to identify students who are at potential risk and help them to develop best course of action which would eventually enhance the performance of the whole organisation.
Keywords
Fuzzy Decision Tree, Prediction, Higher Education.References
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