Association Rule Mining in Data Science Institutes in Bangalore

What are the types of association rules?

Undergoing an association rule data science course will help you to know about the different types of association rules available to work in data mining tasks. For instance, the four types of association rules are as follows

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• Quantitative association rule
• Multi-relational association rule
• Interval information association rule
• Generalized association rule

Use cases of association rule mining.

Association rule mining is used largely by data science professionals since, using this algorithm, they can easily detect common correlations, patterns, casual structures, and linkages. Companies and organizations also hire data science professionals with adequate knowledge and skillsets in association rule mining so that the professionals can detect interesting linkages and connections in datasets and help organizations stay ahead of the competition. This technique is used in data mining fields for forecasting and analyzing consumer behavior. Some other use cases of association rule mining are catalog design, Market Basket analysis, customer analytics, shop layout, product clustering, etcetera. Programmers also require association rule mining techniques for developing machine learning programs.

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Advantages of using association rule mining

Association rule mining benefits in the data-centric world are immense. It is one of the most critical data mining techniques enabling data science professionals to solve problems and business challenges. The prime benefits of association rule mining technique in the field of data mining and data science are as follows

• Enables companies and organizations to gather accurate and reliable information
• It is a cost-effective and efficient data mining technique compared to other types of data applications
• Uses legacy and new systems
• Enables businesses to make operational adjustments and profitable production
• Detects fraudulent activities and other risks
• Enables data science professionals to detect fraud, improve product safety, and build risk models

What are the different algorithms for association rule mining?

Many algorithms are used for generating associate rules in data mining, such as the following

Apriori Algorithm

This algorithm identifies frequently occurring individual items in a database and expands the items to more extensive item sets. 

Eclat Algorithm

This algorithm is also referred to as the equivalence class clustering algorithm. Apart from the Apriori algorithm, Lattice traversal is also a widely used associate rule method in data mining. Many people consider this algorithm to be an efficient and better algorithm than apriori.

FP-growth Algorithm

FP growth algorithm is a recurring pattern, and this association rule mining algorithm helps detect frequent patterns without requiring candidate generation. It operates mainly in two phases FP-tree construction phase and constructing frequently used item sets.

Application of associate rule mining in different sectors

• Healthcare sector 

The association rule mining technique is used in medical diagnosis and helps data science professionals identify the likelihood of diseases and illnesses concerning various symptoms and factors. This data-learning technique enables physicians to cure patients quickly. By leveraging the association rule data mining technique, the healthcare sector can eliminate errors and mistakes in medical diagnoses and provide reliable and accurate results. Data science professionals working in the healthcare sectors can leverage association rules to understand the relationship between new symptoms and the corresponding illness or diseases.

• Government sector

The government makes census data available to plan public services, which include transport, health, and education. Census data also includes public businesses like shopping malls, new factories, marketing products, etcetera. Data science professionals apply association rule mining techniques and data mining approaches to support sound public services and policies and to ensure the efficient functioning of the society.

• Market Basket analysis 

A typical and most common example of association rule mining is Market Basket analysis. Data is extracted in many supermarkets with the help of barcode scanners. This type of database is known as the Market Basket database, which comprises massive customer records and past transactions. A considerable amount of data is collected from shopping markets that detect customer patterns and are a widely used modeling approach today.