Abstract
HSES knowledge portal system is one of the web portals. In this portal, we collect information into student, teacher, and other datasets. Now we combine these datasets with common features with class descriptors. This is named as STO dataset which is handled using traditional tools and techniques. This large dataset is generally known as big data. Increasing the use of social media and new innovations generate a vast amount of data. The quantity of data makes HSES-KP very powerful and useful. This portal needs to manage and analyze big data for better association rules and outcomes. Association rule mining is the technique for finding frequent patterns, associations, correlations, or causal structures among sets of items in transaction databases. Lift, support, and confidence are user-defined measures of interestingness. In this paper, we review the background and the various techniques of association rules mining. This study grows up with a discussion of challenges and future directions.
Published Version
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