Present State-of-The-Art of Association Rule Mining Algorithms
N.Satyavathi1, B.Rama2, A.Nagaraju3
1N.Satyavathi*, CSE Department, Vaagdevi College of Engineering, Warangal, India.
2Dr. B. Rama, Computer Science, Kakatiya University, Warangal, India.
3Dr. A. Nagaraju, Computer Science, School of Mathematics, Statistics and Computational Science, Central University of Rajasthan, Ajmer, India.
Manuscript received on September 28, 2019. | Revised Manuscript received on October 30, 2019. | Manuscript published on October 30, 2019. | PP: 6398-6405 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2202109119/2019©BEIESP | DOI: 10.35940/ijeat.A2202.109119
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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: A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis.
Keywords: Association Rule, Apriori, Pattern-Growth, Support.