The Eclat algorithm is a depth search algorithm frequently used in data mining and is often used to find frequently occurring sets of items in a data set. This is a similar goal to the Apriori algorithm, but Eclat uses a different approach.
The Eclat algorithm uses the orthogonal data format to determine the frequency of itemsets. That is, it stores which transactions each item was involved in. This is an approach unlike Apriori's horizontal format, which stores which elements were involved in each transaction.
The Eclat algorithm usually consists of two steps:
Lists all operations of single items and performs operations on these lists.
It creates larger sets of items and calculates their frequency, discarding those below a certain threshold.
This process continues until no more itemsets can be created. As a result, frequently occurring item clusters are identified.
The Eclat algorithm is generally faster than Apriori because it makes fewer comparisons and uses less memory. However, it can still be slow on very large data sets because there is a need to calculate the frequency of entire sets of items.
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