An improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules

Abstract

This research focuses on mining the frequent patterns occurred in the given Datasets by Generalization of Association Rules. Frequent pattern mining is a significant as well as interesting problem in the research filed of Data Mining. Building of frequent pattern tree (FP tree), frequent pattern growth (FP growth) and association rule generation are implemented to find the repeated patterns of data. FP tree Construction Algorithm is mainly used to compact a vast database into a extremely compressed, seems to very tiny data structure; hence eliminates the repeated scanning of database. The role of FP growth algorithm is to discover the frequent patterns with FP tree structure and construct the generalized association rules corresponding to the learning data. FP growth algorithm obtained best results as compared with conventional Apriori algorithm. This research provides some practical real time applications pertaining data mining techniques in the field of learning, education, marketing, finance and so on.

Multimedia Data Mining and Processing for News Source Attribution

“…ARL finds a relationship that exists between variables or looks for patterns based on the connection of a particular event to other events [28]. ARL can be used in several domains (large databases of one thousand to one million datasets as well as small datasets) to find associations, and frequent patterns from the sets of objects in datasets [29]. It is a commonly used method for heart disease prediction as it produces the correlation of different features for examination and categorizing outpatients with all risk factors needed for prediction [30].…”