Open AccessAn improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1118.1292s219
Subject(s) - association rule learning , apriori algorithm , data mining , generalization , computer science , construct (python library) , tree (set theory) , gsp algorithm , k optimal pattern discovery , field (mathematics) , affinity analysis , machine learning , artificial intelligence , mathematics , mathematical analysis , pure mathematics , programming language
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.