Augmenting Association Rule Mining in Apriori Algorithm using Cuckoo Search with Opposition Parameters-Based Learning
| International Journal of Computer Science and Engineering |
| © 2024 by SSRG - IJCSE Journal |
| Volume 11 Issue 9 |
| Year of Publication : 2024 |
| Authors : N. Bhanu Prakash, E. Kesavulu Reddy |
10.14445/23488387/IJCSE-V11I9P104
How to Cite?
N. Bhanu Prakash, E. Kesavulu Reddy, "Augmenting Association Rule Mining in Apriori Algorithm using Cuckoo Search with Opposition Parameters-Based Learning," SSRG International Journal of Computer Science and Engineering , vol. 11, no. 9, pp. 26-38, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I9P104
Abstract:
Data mining extracts hidden patterns from large datasets, making the information extracted useful for improving decisions and, hence, business outcomes. Among these methods, frequent itemset mining is a very popular and core technique within association rule mining The Apriori algorithm is one of the most popular algorithms in this area of frequent itemset and association rule discovery. Applications include market basket analysis, educational course selection, stock management, and medical data analysis. However, large datasets are exponentially increasing the computational burden of the Apriori algorithm, and hence, execution on parallel-distributed environments can improve performance. The improved approach presented in this paper integrates the Apriori algorithm with the Cuckoo Search algorithm using opposition parameters-based learning (CSOPBL). The Cuckoo Search mechanism with opposition-based learning efficiently prunes the transactions and items in each transaction. It is an approach whose processing time is greatly reduced if executed on a Spark in-memory distributed environment. The experimental results showed that the proposed CS-OPBL-based method outperforms the competing algorithms; for example, at a minimum support threshold of 0.75%, the processing time of this approach is only about 5.8% of that by using the state-of-the-art method on the retail dataset.
Keywords:
Data Mining, Frequent Itemset Mining (FIM), Association Rule Mining, Apriori Algorithm, Cuckoo Search and Spark.
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