In higher education the performance of students is a most challenge work day by day in academic as well as in other curricular activities. As they all know that internet technology is growing as much as faster, but the learning approach of students are not up to the mark. The emerging research community which helps to find the solution to the said problem is Educational Data Mining. In present scenario, the huge students' data is stored in educational database. That type of database contains widely open or secret information to improve student performance. In our proposed work, we will have tested it on reputed dataset, which can be downloaded from a well known organization UCI repository and dataset name is student-mat.csv. This work has been investigated the process of classification of plethora of student's data. Classification plot data into pre-determined groups of classes. It is often mentioned to as supervised learning because the classes are determined before analyzing the data. The work will to be divided into two parts. The first part will be the entropy based feature selection, after that classification process has to be performed. For the classification, we would have used 2 level classification method i.e, SVM and KNN. Later than observe the performance prediction of students based on parameters like accuracy, sensitivity, specificity of proposed method and is to be compared with some previous methods results.
Knowledge extraction is a process of filtering some informative knowledge from the database so that it can be used wide variety of applications and analysis. Due to this highly efficient algorithm is required for data mining and for accessing data from large datasets. In frequent item sets are produced from very big or huge data sets by applying some rules or association rule mining algorithms like Apriori technique, Partition method, Pincer-Search, Incremental, Border algorithm and many more, which take larger computing time to calculate all the frequent itemsets. As the network traffic increases we need an efficient system to monitor packet analysis of network flow data. Due to this frequent itemsets mining is basic problem in field of data mining and knowledge discovery. Here in this paper a brief survey of all the techniques related to frequent item sets generation has been given.
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