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Foundations of Artificial Intelligence & Machine Learning

Licence
OPEN CC-BY-4.0
Authors
Nidhi Sharma, Honey Singh, Ajay Sharma, Deepak Dagar
Published
2026-07-28 · Zenodo
Language
eng
Length
37166 words
Type
narrative text
Open ↗ Download Open original ↗
Support and Confidence

Association rules are commonly measured using two important concepts: support and confidence. Support Support measures how frequently an item combination appears in the dataset. Higher support indicates that the relationship occurs more often. Confidence

Confidence measures how strongly two items are associated with each other. For example, if customers purchasing coffee frequently buy sugar as well, the confidence value for that relationship becomes high. These measures help identify useful and reliable patterns. Applications of Association Rule Learning Association Rule Learning is used in many real-world applications such as:

  • product recommendation systems
  • online shopping analysis
  • medical pattern discovery
  • website usage analysis Streaming platforms and e-commerce websites often use similar techniques to suggest related products or content. Advantages and Limitations Association Rule Learning helps organizations discover hidden patterns that may not be visible through manual analysis. It improves decision-making and supports intelligent recommendation systems. However, very large datasets may generate huge numbers of rules, making analysis more complex. Some discovered relationships may also be weak or practically unimportant. Despite these limitations, Association Rule Learning remains an important unsupervised learning technique for pattern discovery and customer behavior analysis in modern AI systems.

CHAPTER 7

NEURAL NETWORKS AND DEEP LEARNING