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What are the advantages of Apriori algorithm?
The pros of Apriori are as follows:
- This is the most simple and easy-to-understand algorithm among association rule learning algorithms.
- The resulting rules are intuitive and easy to communicate to an end user.
What is a priori principle?
A priori, Latin for “from the former”, is traditionally contrasted with a posteriori. Whereas a posteriori knowledge is knowledge based solely on experience or personal observation, a priori knowledge is knowledge that comes from the power of reasoning based on self-evident truths.
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).
How are frequent 1 itemsets found in apriori?
Apriori employs an iterative approach known as level-wise search, where k-itemsets are used to explore (k+1)-itemsets. First, the set of frequent 1-itemsets is found by scanning the database to accumulate the count for each item, and collecting those items that satisfy minimum support.
Which is the best algorithm for boosting data?
AdaBoost is a boosting algorithm which constructs a classifier. As you probably remember, a classifier takes a bunch of data and attempts to predict or classify which class a new data element belongs to. kNN, or k-Nearest Neighbors, is a classification algorithm.