Present State-of-The-ART of Dynamic 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 October 14, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 309-316 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4107119119/2019©BEIESP | DOI: 10.35940/ijitee.A4107.119119
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Abstract: Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.
Keywords: Data Mining, Incremental Association Rule Mining, Apriori-based, Pattern-Growth ARM algorithms.
Scope of the Article: Data Mining