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Why Apriori algorithm is not efficient?
Further, Apriori algorithm also scans the database multiple times to calculate the frequency of the itemsets in k-itemset. So, Apriori algorithm turns out to be very slow and inefficient, especially when memory capacity is limited and the number of transactions is large.
What are the drawbacks of Apriori algorithm How can you improve efficiency of Apriori?
The major drawback with Apriori algorithm is of time and space. It generates numerous uninteresting itemsets which lead to generate various rules which are of completely of no use. The two factors considered for association rules generation are Minimum Support Threshold and Minimum Confidence Threshold.
How can the efficiency of an Apriori algorithm be improved?
Based on the inherent defects of Apriori algorithm, some related improvements are carried out: 1) using new database mapping way to avoid scanning the database repeatedly; 2) further pruning frequent itemsets and candidate itemsets in order to improve joining efficiency; 3) using overlap strategy to count support to …
What is Apriori algorithm efficiency?
Apriori is the key algorithm in association rule mining. In this paper, we are proposing a method to improve Apriori algorithm efficiency by reducing the database size as well as reducing the time wasted on scanning the transactions.
Why is the algorithm called the Apriori algorithm?
Apriori Algorithm. Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. We apply an iterative approach or level-wise search where k-frequent itemsets are used
How to generate candidate set C2 using Apriori algorithm?
(II) compare candidate set item’s support count with minimum support count (here min_support=2 if support_count of candidate set items is less than min_support then remove those items). This gives us itemset L1. Generate candidate set C2 using L1 (this is called join step).
When to use the same test in apriori?
If A->B and B->A are the same in Apriori, the support, confidence and Lift should be the same. Hence, If you evaluate the results in Apriori, you should do some test like Jaccard, consine, Allconf, Maxconf, Kulczynski and Imbalance ratio.