Optimization of Association Rules for Students’ Data: A Soft Set
Sasanko Sekhar Gantayat

Sasanko Sekhar Gantayat, Department of Computer Science and Engineering GMR Institute of Technology, Rajam, Andhra Pradesh, India.
Manuscript received on 06 July 2019 | Revised Manuscript received on 10 July 2019 | Manuscript published on 30 July 2019 | PP: 3453-3458 | Volume-8 Issue-9, July 2019 | Retrieval Number: F3616048619/19©BEIESP | DOI: 10.35940/ijitee.I7651.078919

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Association rule (AR) mining is a common method to find the associations between objects in a transaction or in a data set, which is simple logical rules, determine the critical relationship among the objects. The rule generation is difficult if it satisfies a predefined threshold value. To trace the relationship is very important for domains to reveal crucial and hidden information from the data set. Some critical relationship of the objects may appear rarely and to trace them using classical methods are difficult one. The apriori algorithm (AA) with hashing technique along with the PDI algorithm is used to find the appropriate ARs from the students’ dataset. Since soft set is a tool for handling imprecise parameterized data, the soft set concept is used in optimization process to get optimized ARs. In this paper, the ARs are optimized using soft set for further analysis.
Index Terms: Association Rule (AR), Data Mining, Hash Based Apriori Algorithm (AA), PDI, Soft Sets

Scope of the Article: Data Mining