A pragmatic approach to recover access time of Apriori algorithm by applying intersection on CSS for redefining FIS through matrix implementation in textual data

Abstract

Nowadays data analytics OLAP (online Analytical Processing) is mostly accepted domain of current researchers and the concept of data mining serves better for the same. There are so many data mining methodologies defined for data analytics. Mining Association rule is widely used in data mining methods for data categorization. Apriori Algorithm is popular method for defining n-element. Frequent item set form k number of huge transactional data set online transaction processing (OLTP) using Association Mining rule (AMR). In this paper, researchers executed original Apriori on transactional data set containing 35039 number of transactions, divided into three data sets DS-1 to DS-3 with 20039, 12000, 5000 number of transactions with variable length with minimum support of 30%, 60% and 80% respectively. Researchers carried out experimental work and compared results of Apriori Algorithm with our proposed algorithm (enhanced version of Apriori algorithm) on the same perimeter and state improvement with 11%, 30% and 27% of Rate of Improvements in DS-1 to Ds-3 respectively for 30% minimum support. Our proposed algorithm is working far much better then Apriori algorithm at each parameter which was included to conclude the results.

Subject Classification:

• Primary 93A30
• Secondary 49K15

Keywords:

• Apriori
• Minimum threshold
• OLAP
• OLTP
• Frequent item set
• Matrix