IMPROVED EVENT DATA SCHEDULING FRAMEWORK THROUGH OPTIMIZED FP-GROWTH ALGORITHM
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Abstract
Education is the important tool and plays a vital role in the nation development. In recent days, modern technologies are used for the educational tool. Nevertheless the students and trainers should be trained thoroughly about their subjects. In this work, data mining techniques help to perform decision making such as who are the trainee for the program and which are the skill programs need to be offer and schedule the training sessions based on the trainee time schedule. Officially the events can be conducted for the students as well as for the faculties. In this proposed work, a novel algorithm will be produced for the event data scheduler using Association Rule Mining. The Optimized Frequent Pattern Growth algorithm can be proposed to obtain the market basket analysis. The main contribution of the proposed work is to obtain the optimized decision making for the training sessions to make the event scheduling with improved performance. Thus the decision making can be obtained through the knowledge obtained from the behavior patterns identified by the heuristics detector. The behavior pattern helps to provide efficient event data scheduling framework those who are in the educational sector. The experiment results can be generated for the analyzing the performance and effectiveness of the proposed work in the tools defined for the data mining using education dataset as evaluation values for teaching assistant from UCI repository.
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