An improved version of DF , the depth first implementation of Apriori as devised in [7], is presented. Given a database of (e.g., supermarket) transactions, the DF algorithm builds a so-called trie that contains all frequent itemsets, i.e., all itemsets that are contained in at least minsup transactions with minsup a given threshold value. In the trie, there is a one-to-one correspondence betwe...

BACKGROUND Promoting family planning (FP) is a key strategy for health, economic and population growth. Sub-Saharan Africa, with one of the lowest contraceptive prevalence and highest fertility rates globally, contributes half of the global maternal deaths. Improving the quality of FP services, including enhancing pre-service FP teaching, has the potential to improve contraceptive prevalence. I...

Data mining is a technique of analyzing the dataset such that the final conclusion can be accessed easily and quickly from the dataset. Here in this paper association rule mining technique is implemented for the analysis of agricultural dataset. The idea is to apply association rule mining technique for generating rules and establishing a relationship between them .So as to enhance crop product...

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. In this paper, we present the performance comparison of Apriori and FP-growth algorithms. The performance is analyzed based on the execution time...