Apriori Algorithm.pptx

•Apriori Algorithm
in Data Mining

What Is An Itemset?
• A set of items together is called an itemset. If any itemset has k-
items it is called a k-itemset. An itemset consists of two or more
items. An itemset that occurs frequently is called a frequent
itemset. Thus frequent itemset mining is a data mining technique to
identify the items that often occur together.
• For Example, Bread and butter, Laptop and Antivirus software, etc.

What Is A Frequent Itemset?
• A set of items is called frequent if it satisfies a minimum threshold
value for support and confidence. Support shows transactions with
items purchased together in a single transaction. Confidence shows
transactions where the items are purchased one after the other.
For frequent itemset mining method, we consider only those
transactions which meet minimum threshold support and
confidence requirements. Insights from these mining algorithms
offer a lot of benefits, cost-cutting and improved competitive
advantage.
There is a tradeoff time taken to mine data and the volume of data
for frequent mining. The frequent mining algorithm is an efficient
algorithm to mine the hidden patterns of itemsets within a short
time and less memory consumption.
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Frequent Pattern Mining (FPM)
• The frequent pattern mining algorithm is one of the most important
techniques of data mining to discover relationships between
different items in a dataset. These relationships are represented in
the form of association rules. It helps to find the irregularities in
data.
FPM has many applications in the field of data analysis, software
bugs, cross-marketing, sale campaign analysis, market basket
analysis, etc.
Frequent itemsets discovered through Apriori have many
applications in data mining tasks. Tasks such as finding interesting
patterns in the database, finding out sequence and Mining of
association rules is the most important of them.
Association rules apply to supermarket transaction data, that is, to
examine the customer behavior in terms of the purchased products.
Association rules describe how often the items are purchased
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Association Rules
• Association Rule Mining is defined as:
“Let I= { …} be a set of ‘n’ binary attributes called items.
Let D= { ….} be set of transaction called database. Each
transaction in D has a unique transaction ID and
contains a subset of the items in I. A rule is defined as an
implication of form X->Y where X, Y? I and X?Y=?. The
set of items X and Y are called antecedent and consequent
of the rule respectively.”
Learning of Association rules is used to find relationships between
attributes in large databases. An association rule, A=> B, will be of
the form” for a set of transactions, some value of itemset A
determines the values of itemset B under the condition in which
minimum support and confidence are met”.
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• Support and Confidence can be represented by the following example:
Bread=> butter [support=2%, confidence-60%]
The above statement is an example of an association rule. This means that
there is a 2% transaction that bought bread and butter together and there
are 60% of customers who bought bread as well as butter.
• Support and Confidence for Itemset A and B are represented by formulas:
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1.
2.
Association rule mining consists of 2 steps:
Find all the frequent itemsets.
Generate association rules from the above frequent itemsets.

Why Frequent Itemset Mining?
• Frequent itemset or pattern mining is broadly used
because of its wide applications in mining association
rules, correlations and graph patterns constraint that is
based on frequent patterns, sequential patterns, and
many other data mining tasks.

Apriori Algorithm – Frequent Pattern
Algorithms
Apriori algorithm was the first algorithm that was proposed for frequent
itemset mining. It was later improved by R Agarwal and R Srikant and came
to be known as Apriori. This algorithm uses two steps “join” and “prune” to
reduce the search space. It is an iterative approach to discover the most
frequent itemsets.
Apriori says:
The probability that item I is not frequent is if:
P(I) < minimum support threshold, then I is not frequent.
P (I+A) < minimum support threshold, then I+A is not frequent, where A
also belongs to itemset.
If an itemset set has value less than minimum support then all of its
supersets will also fall below min support, and thus can be ignored. This
property is called the Antimonotone property.
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The steps followed in the Apriori Algorithm of data mining
are:
• Join Step: This step generates (K+1) itemset from K-itemsets by
joining each item with itself.
• Prune Step: This step scans the count of each item in the database.
If the candidate item does not meet minimum support, then it is
regarded as infrequent and thus it is removed. This step is
performed to reduce the size of the candidate itemsets.

Steps In Apriori
Apriori algorithm is a sequence of steps to be followed to find the most
frequent itemset in the given database. This data mining technique follows
the join and the prune steps iteratively until the most frequent itemset is
achieved. A minimum support threshold is given in the problem or it is
assumed by the user.
• #1) In the first iteration of the algorithm, each item is taken as a 1-itemsets
candidate. The algorithm will count the occurrences of each item.
• #2) Let there be some minimum support, min_sup ( eg 2). The set of 1 –
itemsets whose occurrence is satisfying the min sup are determined. Only
those candidates which count more than or equal to min_sup, are taken
ahead for the next iteration and the others are pruned.

• #3) Next, 2-itemset frequent items with min_sup are discovered. For this
in the join step, the 2-itemset is generated by forming a group of 2 by
combining items with itself.
• #4) The 2-itemset candidates are pruned using min-sup threshold value.
Now the table will have 2 –itemsets with min-sup only.
• #5) The next iteration will form 3 –itemsets using join and prune step. This
iteration will follow antimonotone property where the subsets of 3-itemsets,
that is the 2 –itemset subsets of each group fall in min_sup. If all 2-itemset
subsets are frequent then the superset will be frequent otherwise it is
pruned.
• #6) Next step will follow making 4-itemset by joining 3-itemset with itself
and pruning if its subset does not meet the min_sup criteria. The algorithm
is stopped when the most frequent itemset is achieved.

• Example of Apriori: Support threshold=50%, Confidence= 60%

TABLE-1
Solution:
Support threshold=50% => 0.5*6= 3 => min_sup=3

2. Prune Step: TABLE -2 shows that I5 item does not meet min_sup=3,
thus it is deleted, only I1, I2, I3, I4 meet min_sup count.
TABLE-3

3. Join Step: Form 2-itemset. From TABLE-1 find out the
occurrences of 2-itemset.
TABLE-4

4. Prune Step: TABLE -4 shows that item set {I1, I4} and {I3, I4}
does not meet min_sup, thus it is deleted.
TABLE-5

• 5. Join and Prune Step: Form 3-itemset. From the TABLE- 1 find out
occurrences of 3-itemset. From TABLE-5, find out the 2-itemset subsets which
support min_sup.
We can see for itemset {I1, I2, I3} subsets, {I1, I2}, {I1, I3}, {I2, I3} are occurring
in TABLE-5 thus {I1, I2, I3} is frequent.
We can see for itemset {I1, I2, I4} subsets, {I1, I2}, {I1, I4}, {I2, I4}, {I1, I4} is not
frequent, as it is not occurring in TABLE-5 thus {I1, I2, I4} is not frequent, hence it
is deleted.
TABLE-6
Only {I1, I2, I3} is frequent.

6. Generate Association Rules: From the frequent itemset discovered
above the association could be:
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{I1, I2} => {I3}
Confidence = support {I1, I2, I3} / support {I1, I2} = (3/ 4)* 100 = 75%
{I1, I3} => {I2}
{I2, I3} => {I1}
{I1} => {I2, I3}
Confidence = support {I1, I2, I3} / support {I1} = (3/ 4)* 100 = 75%
{I2} => {I1, I3}
Confidence = support {I1, I2, I3} / support {I2 = (3/ 5)* 100 = 60%
{I3} => {I1, I2}
Confidence = support {I1, I2, I3} / support {I3} = (3/ 4)* 100 = 75%
This shows that all the above association rules are strong if minimum
confidence threshold is 60%.
• The Apriori Algorithm: Pseudo Code
C: Candidate item set of size k
L: Frequent itemset of size k

Advantages
• Easy to understand algorithm
• Join and Prune steps are easy to implement on large
itemsets in large databases
Disadvantages
• It requires high computation if the itemsets are very
large and the minimum support is kept very low.
• The entire database needs to be scanned.

Methods T
o Improve Apriori
Efficiency
• Hash-Based Technique: This method uses a hash-based structure called
a hash table for generating the k-itemsets and its corresponding count. It
uses a hash function for generating the table.
Transaction Reduction: This method reduces the number of
transactions scanning in iterations. The transactions which do not contain
frequent items are marked or removed.
Partitioning: This method requires only two database scans to mine the
frequent itemsets. It says that for any itemset to be potentially frequent in
the database, it should be frequent in at least one of the partitions of the
database.
Sampling: This method picks a random sample S from Database D and
then searches for frequent itemset in S. It may be possible to lose a global
frequent itemset. This can be reduced by lowering the min_sup.
Dynamic Itemset Counting: This technique can add new candidate
itemsets at any marked start point of the database during the scanning of
the database.
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Applications Of Apriori Algorithm
• In Education Field: Extracting association rules in data mining of
admitted students through characteristics and specialties.
• In the Medical field: For example Analysis of the patient's
database.
• In Forestry: Analysis of probability and intensity of forest fire
the forest fire data.
with
• Apriori is used by many companies like Amazon in
the Recommender System and by Google for the auto-complete
feature.

Apriori Algorithm.pptx

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Apriori Algorithm.pptx