Improvisation in Efficiency of Apriori Algorithm for Mining Frequent Itemsets
DOI:
https://doi.org/10.26438/ijcse/v6i8.424428Keywords:
Itemsets, Apriori algorithm, Association rule mining, Minimum supportAbstract
Association rule mining is a procedure which is meant to find frequent patterns from data sets found in various kinds of databases such as relational databases, transactional databases, etc. It has a great importance in data mining. Extracting relevant information from a huge collection of data by exploitation of data is called data mining. There is an increasing need of data mining by business people to extract valid and useful information from large datasets. Thus, data mining has its importance to discover hidden patterns from huge data stored in databases as well as data warehouse. Apriori algorithm has been one of the key algorithms in association rule mining. Classical Apriori algorithm is inefficient as it takes considerable amount of time to generate the desired output for mining the frequent itemsets owing to multiple scans on the database. In this research paper, a method has been proposed to improve the efficiency of Apriori algorithm by reducing the size of the database as well as reducing the time complexity for scanning the transactions
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