Modified Associative Algorithm to Determine Frequent Pattern from Student Dataset
Kamalpreet Kaur1, Kiranbir Kaur2

1Kamalpreet Kaur*, Computer and Science Department, Guru Nanak Dev University, Amritsar, India.
2Kiranbir Kaur, Computer and Science Department, Guru Nanak Dev University, Amritsar, India.

Manuscript received on March 30, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 2449-2452 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8321049420/2020©BEIESP | DOI: 10.35940/ijeat.D8321.049420
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Abstract: The phenomenal advances in Students produces huge amount of data like MOOC data and high throughput information that makes Electronic Student records (ESRs) expensive and complex. For the analysis of such a huge amount of data, AI and data mining techniques have been utilized along with Student services. Today, Data mining is utilized to detect performances using various informational datasets along with machine learning algorithms. There are many techniques available which are utilized for diagnosis of student performance like FP growth, Apriori and Associative algorithm etc. These techniques discover unknown patterns or relationships from large amount of data and these are utilized for making decisions for preventive and suggestive medicine. The main disadvantage of these techniques is it discovers fewer patterns. In this paper we proposed modified associative algorithm that discovers patterns to detect performance accurately. The results will help in predicting the performance quicker and more accurately, so that it leads to timely aware the students.
Keywords: Data Mining, Associative, Accuracy