Apriori algorithm: Difference between revisions

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== Overview ==

The Apriori algorithm was proposed by Agrawal and Srikant in 1994. Apriori is designed to operate on [[database]]s containing transactions (for example, collections of items bought by customers, or details of a website frequentation or [[IP address]]es<ref>[https://deductive.com/blogs/data-science-ip-matching/ The data science behind IP address matching] {{Webarchiveusurped|url1=[https://web.archive.org/web/20210822191810/https://deductive.com/blogs/data-science-ip-matching/ |date=2021-08-22The data science behind IP address matching]}} Published by deductive.com, September 6, 2018, retrieved September 7, 2018</ref>). Other algorithms are designed for finding association rules in data having no transactions ([[Winepi]] and Minepi), or having no timestamps ([[DNA sequencing]]). Each transaction is seen as a set of items (an ''itemset''). Given a threshold <math>C</math>, the Apriori algorithm identifies the item sets which are subsets of at least <math>C</math> transactions in the database.

Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as ''candidate generation''), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found.

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'''Apriori'''(T, ε)

L₁ ← {large 1 -singleton itemsets}

k ← 2

'''while''' L_k−1 '''is not''' empty

C_k ← Apriori_genGenerate_candidates(L_k−1, k)

'''for''' transactions t '''in''' T

D_t ← {c in C_k : c ⊆ t}

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k ← k + 1

'''return''' Union(L_k) '''over all''' k

'''Apriori_genGenerate_candidates'''(L, k)

result ← listempty_set()

'''for all''' p ∈ L, q ∈ L '''where''' p₁ =and q₁, p₂differ =in q₂,exactly ...,one p_k-2 = q_k-2 and p_k-1 < q_k-1element

c ← p ∪ {q_k-1}

'''if''' u ∈ L '''for all''' u ⊆ c '''where''' |u| = k-1

result.add(c)

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{| class="wikitable"

|-

| &alpha; || &beta; || theta&epsilon;

|-

| &alpha; || &beta; || epsilon&theta;

|-

| &alpha; || &beta; || theta&epsilon;

|-

| &alpha ; || &beta; || epsilon&theta;

|-

| alpha|| beta || theta

|}

The association rules that can be determined from this database are the following:

# 100% of sets with &alpha; also contain &beta;

# 50% of sets with &alpha;, &beta; also have &epsilon;

# 50% of sets with &alpha;, &beta; also have &theta;

we can also illustrate this through a variety of examples.

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Also, both the time and space complexity of this algorithm are very high: <math>O\left(2^{|D|}\right)</math>, thus exponential, where <math>|D|</math> is the horizontal width (the total number of items) present in the database.

Later algorithms such as [[Max-Miner]]<ref>{{cite journal|author=Bayardo Jr, Roberto J.|title=Efficiently mining long patterns from databases|journal=ACM SIGMOD Record |volume=27|issue=2|year=1998|pages=85–93 |doi=10.1145/276305.276313 |url=http://www.cs.sfu.ca/CourseCentral/741/jpei/readings/baya98.pdf}}</ref> try to identify the maximal frequent item sets without enumerating their subsets, and perform "jumps" in the search space rather than a purely bottom-up approach.

== References ==