What is the difference between Apriori and Eclat algorithms?

Question

What is the difference between Apriori and Eclat algorithms in association rule mining?

score 3 · Answer 1 · answered Aug 24 '15 at 19:16

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Here is a good description: http://www.slideshare.net/wanaezwani/apriori-and-eclat-algorithm-in-association-rule-mining In particular, apriori is probably the first association rule mining and computationally complex. This leads to the introduction of further fast algorithms.

answered Aug 24 '15 at 19:16

Reza Rawassizadeh

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I guess Reza has provided the meanings in data mining community for apriori and eclat. Webster defines apriori as "presupposed by experience" and eclat as "brilliant or dazzling effect". I can see how eclat could mean a clever and efficient algorithm. I have a hard time relating the definition of apriori with a computationally complex and slow algorithm. It should be pointed out that in Bayesian statistics apriori corresponds knowledge about a parameter's possible values before collecting data. While eclat is not a term commonly used in statistics. – Michael R. Chernick Dec 31 '16 at 01:37

score 1 · Answer 2 · edited Feb 23 '20 at 15:25

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Apriori is useable with large datasets and Eclat is better suited to small and medium datasets.
Apriori scans the original (real) dataset, whereas Eclat scan the currently generated dataset.
Apriori is slower than Eclat.

edited Feb 23 '20 at 15:25

dourouc05

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answered Feb 02 '16 at 10:08

Subhash

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score 0 · Answer 3 · answered Nov 14 '19 at 06:49

Apriori algorithm is a classical algorithm used to mining the frequent item sets in a given dataset.
Coming to Eclat algorithm also mining the frequent itemsets but in vertical manner and it follows the depth first search of a graph.
As per the speed,Eclat is fast than the Apriori algorithm.
Apriori works on larger datasets where as Eclat algorithm works on smaller datasets.

score 0 · Answer 4 · answered Nov 10 '20 at 14:06

Look this article:
Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms

Apriori is an easily understandable frequent itemset mining algorithm. Because of this, Apriori is a popular starting point for frequent itemset study. However, Apriori has serious scalability issues and exhausts available memory much faster than Eclat and FP-Growth. Because of this Apriori should not be used for large datasets.

What is the difference between Apriori and Eclat algorithms?

4 Answers4