Question
- What is the association rule in data mining?
- Why is the association rule especially important in big data analysis?
- How does the association rule allow for more advanced data interpretation?
Answer
1. What is the association rule in data mining?
Association rule mining is a technique for detecting similar themes, patterns, and links in
information stored in various databases, including relational database systems, procedural
database systems, and other types of databases.
At its most basic level, association rule mining uses machine learning models to evaluate
data from a database for correlations or co-occurrences. It detects common if-then relationships,
which are association rules in and of themselves. There are two pieces to an apriori algorithm:
antecedents (if) and a consequence (if) (then). An antecedent is anything that appears in the data
(Manthri, 2018). An object encountered in conjunction with the antecedents is known as a
consequent.
The link between nappies and beverages is an illustration of association rule mining in
action. According to the example, people who go to a shop to purchase nappies are also likely to
buy alcohol, which appears to be fictitious. The following are some examples of data that might
be used to support that claim. There are 100,000 client transactions at a supermarket. The
purchase of diapers accounts for around 2,000 transactions or approximately 1% of all trades.
Beer is purchased in about 2,750 occurrences (1.4 percent). A total of 1,750 transactions, or 0.9
percent, comprise both diaper and beer purchases. That enormous figure should be substantially
lower based on the percentages (Abdel-Basset et al., 2018). The fact that around 43% of diaper
purchases also involve a beer purchase suggests a relationship between nappies and drinking.
2. Why is the association rule especially important in big data analysis?
For extracting association rule mining, data mining tools give a viable alternative. On the
other hand, these systems generate a vast number of rules, preventing decision-makers from
selecting the most exciting regulations independently. To address this issue, decision-makers
dealing with a high number of extracted rules might benefit from the inclusion of multi-criteria
strategic planning within the large volume of data (Manthri, 2018). The vast number of extracted
rules using Spark's PFP-growth algorithms necessitated the employment of a multi-criteria
assessment ranking technique that deals with many options in this approach.
The mining method for discovering undiscovered patterns in massive transaction
databases is computationally costly. When mining within memory, one major problem is the size
of the transaction dataset, which can be in the millions or billions. Further, sophisticated out-of-
CPU procedures are required to operate on large datasets to locate standard datasets (Manthri,
2018). As a result, this is because convolution and pooling datasets items may raise the danger of
missing common itemset trends.
The association rule is beneficial in big data analysis since it helps to reveal patterns that
are not true since they happened by coincidence. In order to uncover actual rules from unknown
patterns, the latter frequently requires human post-processing or even application domain
knowledge. Finding actionable information from regulations that may improve a product
portfolio, retail configuration, or customer interactions is critical (Abdel-Basset et al., 2018). To
give a more explicit set of rules, association rules can be configured in various ways (for
example, assistance, assurance).
3. How does the association rule allow for more advanced data interpretation?
Understanding client purchase behavior using association rule mining provides a variety
of applications. The guidelines aid in the identification of new possibilities and methods for
cross-selling items to clients. It is utilized for tailored marketing campaigns, more innovative
stock management control, in-store product placement tactics, and improved management
information systems.
Different methods are employed in algorithms to detect frequent datasets to do
association rule mining. The Apriori method is the most well-known, although the FP Growth
method is also often used. There is also a comparable method known as the Maximal Frequent
Itemset Algorithm (MAFIA Algorithm) (Manthri, 2018). All algorithms have various positives
and negatives that must be considered when selecting an algorithm for a specific data
management application.
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