What is Eclat Algorithm?
An introduction to machine learning algorithms
Equivalence Class Clustering and bottom-up Lattice Traversal make up the ECLAT algorithm. It is a well-liked technique for mining Association Rules. I’ll introduce you to the ECLAT algorithm and explain how it operates in this.
What is Eclat Algorithm?
The Eclat Algorithm is also known as bottom up lattice transversal algorithm and equivalence class clustering. A database can be searched using this approach to find common item sets. It is one of the best methods for understanding association rules. While Apriori algorithm utilises breadth first search, Eclat algorithm uses depth first search to find frequent item sets. Unlike the Apriori algorithm, which represents data in a horizontal pattern, it represents the data vertically. The Eclat algorithm is made faster by its vertical layout.
As a result, the Eclat method is a more effective and scalable variant of association rule learning.
How does the algorithm work?
In order to avoid the formation of subsets when the minimal support is obtained, the support value for an item is calculated using this method. Each item-transaction set is compared to every other pair in the function to produce a new candidate. The list of common partners is expanded if this candidate is found to be common. If the frequency set includes a pair of items, the frequent item set also includes its subsets. Obtain the tidlist for each database object. Here, we thoroughly search the database. The list of transactions that contain item “a” is represented by its tidlist. Create a new transaction list whose constituents are transactions in which both item a and item b are involved by intersecting their respective transaction lists. And repeat these above steps again.
Advantages of using Eclat algorithm
- Due to its use of the Depth-First Search methodology, the Eclat algorithm utilises less memory than Apriori.
- Since the Eclat method does not continually scan the data to find frequent itemsets, it is typically quicker than the Apriori approach.
- As long as the dataset is not too large, the Eclat method beats the Apriori algorithm.
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Conclusion
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