An Improved Frequent Pattern Mining in Sustainable Learning Practice using Generalized Association Rules
R. Revathy1, S. Balamurali2
1R. Revathy, Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
2S. Balamurali, Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankoil (Tamil Nadu), India.
Manuscript received on 07 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 776-780 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11181292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1118.1292S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Data Mining, Concept Hierarchy, FP Growth Algorithm, Association Rule.
Scope of the Article: Data Mining