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JAVA/ python/R Frequent Pattern Mining
Algorithms
THEORY:
Frequent pattern mining in data mining is the process of identifying patterns or
associations within a dataset that occur frequently. This is typically done by
analyzing large datasets to find items or sets of items that appear together frequently.
Frequent pattern extraction is an essential mission in data mining that intends to
uncover repetitive patterns or itemsets in a granted dataset. It encompasses
recognizing collections of components that occur together frequently in a
transactional or relational database. This procedure can offer valuable perceptions
into the connections and affiliations among diverse components or features within
the data.
Here’s an elaborate explanation of repeating arrangement prospecting:
 Transactional and Relational Databases:
Repeating arrangement prospecting can be applied to transactional databases, where
each transaction consists of a collection of objects. For instance, in a retail dataset,
each transaction may represent a customer’s purchase with objects like loaf, dairy,
and ovals. It can also be used with relational databases, where data is organized into
multiple related tables. In this case, repeating arrangements can represent
connections among different attributes or columns.
 Support and Repeating Groupings:
The support of a grouping is defined as the proportion of transactions in the database
that contain that particular grouping. It represents the frequency or occurrence of the
grouping in the dataset. Repeating groupings are collections of objects whose
support is above a specified minimum support threshold. These groupings are
considered interesting and are the primary focus of repeating arrangement
prospecting.
 Apriori Algorithm:
The Apriori algorithm is one of the most well-known and widely used algorithms for
repeating arrangement prospecting. It uses a breadth-first search strategy to discover
repeating groupings efficiently. The algorithm works in multiple iterations. It starts
by finding repeating individual objects by scanning the database once and counting
the occurrence of each object. It then generates candidate groupings of size 2 by
combining the repeating groupings of size 1. The support of these candidate
groupings is calculated by scanning the database again. The process continues
iteratively, generating candidate groupings of size k and calculating their support
until no more repeating groupings can be found.
 Support-based Pruning:
During the Apriori algorithm’s execution, aid-based pruning is used to reduce the
search space and enhance efficiency. If an itemset is found to be rare (i.e., its aid is
below the minimum aid threshold), then all its supersets are also assured to be rare.
Therefore, these supersets are trimmed from further consideration. This trimming
step significantly decreases the number of potential item sets that need to be
evaluated in subsequent iterations.
 Association Rule Mining:
Frequent item sets can be further examined to discover association rules, which
represent connections between different items. An association rule consists of an
antecedent and a consequent (right-hand side), both of which are item sets. For
instance, {milk, bread} => {eggs} is an association rule. Association rules are
produced from frequent itemsets by considering different combinations of items and
calculating measures such as aid, confidence, and lift. Aid measures the frequency of
both the antecedent and the consequent appearing together, while confidence
measures the conditional probability of the consequent given the antecedent. Lift
indicates the strength of the association between the antecedent and the consequent,
considering their individual aid.
 Applications:
Frequent pattern mining has various practical uses in different domains. Some
examples include market basket analysis, customer behavior analysis, web mining,
bioinformatics, and network traffic analysis. Market basket analysis involves
analyzing customer purchase patterns to identify connections between items and
enhance sales strategies. In bioinformatics, frequent pattern mining can be used to
identify common patterns in DNA sequences, protein structures, or gene
expressions, leading to insights in genetics and drug design. Web mining can employ
frequent pattern mining to discover navigational patterns, user preferences, or
collaborative filtering recommendations on the web.
Regular pattern extraction is a data extraction approach employed to spot repeating
forms or itemsets in transactional or relational databases. It entails locating
collections of objects that occur collectively often and possesses numerous uses in
different fields. The Apriori algorithm is a well-liked technique utilized to
effectively detect consistent itemsets, and association rule extraction can be carried
out to obtain significant connections between objects.
There are several different algorithms used for frequent pattern mining,
including:
1. Apriori algorithm: This is one of the most commonly used algorithms for
frequent pattern mining. It uses a “bottom-up” approach to identify
frequent itemsets and then generates association rules from those itemsets.
2. ECLAT algorithm: This algorithm uses a “depth-first search” approach to
identify frequent itemsets. It is particularly efficient for datasets with a
large number of items.
3. FP-growth algorithm: This algorithm uses a “compression” technique to
find frequent patterns efficiently. It is particularly efficient for datasets
with a large number of transactions.
4. Frequent pattern mining has many applications, such as Market Basket
Analysis, Recommender Systems, Fraud Detection, and many more.
Advantages:
1. It can find useful information which is not visible in simple data browsing
2. It can find interesting association and correlation among data items
Disadvantages:
1. It can generate a large number of patterns
2. With high dimensionality, the number of patterns can be very large,
making it difficult to interpret the results.

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