Artificial Intelligent Systems and Machine Learning

S. Charles ¹, N. Aarthi ¹

Affiliations
1 Department of Computer Science, St. Joseph's College, Trichy, IN

Abstract

An association rule is a remarkable approach to pull out frequent items from Big Data. This article gives a theoretical overview of association rule-making, Hadoop and MapReduce implementation of the association rule is performed on the different dataset given by the researcher. Further, the association rule based techniques are discussed and the efficiency of algorithms is compared in terms of scale-up, speed up and sizeup measures in big data. The goal of an association is not expected from a random sampling of all possibilities. It might just find relations of items that happen together. The performance of algorithms analyzed with respect to speed up, size up and scale up factors related to the prediction of big data analytics. However, the paper cannot boast to be a complete review of all the research work in an area. In this paper, makeup and offer an appraisal of the work carried out and done by researchers using association rule in Big Data.

Keywords

Big Data, MapReduce, Association Rule, Machine Learning, Hadoop, A-Priori, Size Up, Scale Up Speed Up.

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S. Charles ¹, A. Angelpreethi ²

Affiliations
1 Department of Computer Science, St. Joseph's College, Trichy, Tamilnadu, IN
2 Department of Computer Science, St. Joseph’s College, Trichy, Tamilnadu, IN

Abstract

Data mining refers to mine the knowledge from large amounts of data. It is used to discover some new interesting patterns. Data mining techniques are used to find the association between the IQ performance and Learning behavior of the students. Cluster analysis is used to find the homogeneous data objects. It is used to decide the similarity of the students' data set based on the nature of the learning behavior. Each cluster reveals the identity based on its learning behavior of the student. The intelligence Quotient of the students is evaluated by the Stanford-Binet Intelligence Test and Criterion reference model. Multilayer Perceptron and EM clustering Technique is employed to classify the students based on the Intelligence Level. This experiment analysis could help the staff members to understand the student's behaviour and provide the suitable training for their improvement of academic competence. This paper reveals the intelligent quotient and the learning behaviour of the students in a learning environment.

Keywords

Multilayer Perceptron, Standford-Binet, Criterion Reference Model, Learning Behavior.

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L. Arockiam ¹, V. Arul Kumar ¹, S. Charles ¹

Affiliations
1 Department of Computer Science, St. Joseph's College, Tiruchirappalli-620002, IN

Abstract

Intelligence is defined as an innate ability of an individual to acquire new knowledge. It can be measured by Intelligent Quotient (IQ). In the educational filed, IQ tests are extensively used as indicative measures to find the academic strength of an individual. Debugging is a process of finding the bugs (error) and reducing them in the computer programming. The Program comprehension and debugging skill are useful to predict the student's performance in the programming subjects. The process of extracting valuable knowledge from the raw data is called data mining. In the field of data mining, Association Rule Mining is one of the most popular approaches for discovering the relationship between the items in a dataset. The research work is carried out to derive the relationship between the Intelligent Quotient and Debugging Skills from a student's dataset using Association rule mining technique.

Keywords

Intelligent Quotient, Association Rule Mining, Apriori Algorithm, Support, Confidence.

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