Apriori Algorithm-Explanation of Apriori Algorithm and advantages and Disadvantages of Apriori Algorithm
Apriori Algorithm-
The data mining Apriori algorithm used for mining frequent item set from boolean association rule. That algorithm is the best example of association rule for finding a frequent item from large data set. That algorithm fallows the Bottom-Up approach for finding frequent items. In Apriori algorithm used level wise search approach. Level wise search fallows ‘K’ item set and fallows with ‘K+1’ approach from finding a items.
Set of frequent item set counts are increased and denoted that with R1L1, L1 is used for find L2. The process was repeat until ‘K’ item set not find successively.
The Apriori algorithm search all database for finding a successively frequent item set for future use. The Apriori algorithm are used reduce space technology for fast scanning and implementing result. Apriori algorithm uses large data set property and they easily distributed with easy implementation.
Important Point About Apriori Algorithm-
1.A priority algorithm used association rule mining for frequent item set.
2.Use bottom-Up approach.
3.Used for reduce search space.
4.Required to find frequent item in that algorithm whole database can be searched successively.
Advantages of Apriori Algorithm-
1.Uses large item set property.
2.Easily distributed.
3.Easy to Implement.
Dis-Advantages of Apriori Algorithm-
1.Assumes transaction database is memory resident.
2.Requires Many database scan for perfect result.
Explanation :
The Apriori algorithm is one of the most fundamental and widely used techniques in data mining for discovering frequent itemsets and generating association rules. It was proposed by Rakesh Agrawal and Ramakrishnan Srikant in 1994 and serves as the foundation for many advanced pattern-mining algorithms. The main objective of Apriori is to identify relationships among items in large transactional databases, such as market basket data, where each transaction represents a set of purchased items.
The Apriori algorithm works on the principle that “all non-empty subsets of a frequent itemset must also be frequent.” This property, known as the Apriori principle, helps reduce the search space significantly by eliminating itemsets that cannot possibly be frequent. The algorithm operates in two main steps — frequent itemset generation and rule generation.
In the first step, the algorithm scans the database to find itemsets that satisfy a minimum support threshold. It begins by identifying frequent single items (1-itemsets), then generates candidate itemsets of larger sizes (2-itemsets, 3-itemsets, etc.) by combining smaller frequent ones. At each iteration, itemsets that do not meet the minimum support are pruned. In the second step, association rules are generated from these frequent itemsets based on a minimum confidence threshold, which measures the strength of the relationship between items.
For example, if customers who buy “bread” often buy “butter,” the Apriori algorithm may produce a rule such as {bread} → {butter} with certain support and confidence values. These insights are highly useful for applications like market basket analysis, product recommendation, cross-selling, and inventory management.
Although Apriori is simple and intuitive, it can be computationally expensive for large datasets because it requires multiple database scans and generates many candidate itemsets. To address this limitation, improved versions like FP-Growth and Eclat were developed.
In conclusion, the Apriori algorithm plays a crucial role in data warehousing and data mining by uncovering hidden patterns and associations among data items. Its simplicity, interpretability, and foundational principles make it a cornerstone method in the field of frequent pattern mining.
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