Finding Frequent Itemsets: Difference between revisions

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(Created page with "{{DISPLAYTITLE:Finding Frequent Itemsets (Finding Frequent Itemsets)}} == Description == We assume there is a number $s$, called the support threshold. If $I$ is a set of items, the support for $I$ is the number of baskets for which $I$ is a subset. We say $I$ is frequent if its support is $s$ or more == Parameters == No parameters found. == Table of Algorithms == {| class="wikitable sortable" style="text-align:center;" width="100%" ! Name !! Year !! Time !! Sp...")
 
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== Parameters ==  
== Parameters ==  


No parameters found.
$n$: total number of transactions (size of database)


== Table of Algorithms ==  
== Table of Algorithms ==  
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== Time Complexity graph ==  
== Time Complexity Graph ==  


[[File:Finding Frequent Itemsets - Time.png|1000px]]
[[File:Finding Frequent Itemsets - Time.png|1000px]]
== Space Complexity graph ==
[[File:Finding Frequent Itemsets - Space.png|1000px]]
== Pareto Decades graph ==
[[File:Finding Frequent Itemsets - Pareto Frontier.png|1000px]]

Latest revision as of 09:09, 28 April 2023

Description

We assume there is a number $s$, called the support threshold. If $I$ is a set of items, the support for $I$ is the number of baskets for which $I$ is a subset. We say $I$ is frequent if its support is $s$ or more

Parameters

$n$: total number of transactions (size of database)

Table of Algorithms

Name Year Time Space Approximation Factor Model Reference
A-Priori algorithm 1994 $O(n^{2})$ $O(n^{2})$ Exact Deterministic Time & Space
The Algorithm of Park; Chen; and Yu (PCY) 1995 $O(n^{2})$ $O(n^{2})$ Exact Deterministic Time
The Multistage Algorithm 1999 $O(n^{2})$ $O(n^{2})$ Exact Deterministic Time
The Multihash Algorithm 1999 $O(n^{2})$ $O(n^{2})$ Exact Deterministic Time

Time Complexity Graph

Finding Frequent Itemsets - Time.png