Support

Definition

In data science, support refers to the frequency or proportion of instances in a dataset that contain a specific item or combination of items. It's a crucial concept in association rule mining, as it helps identify the strength and relevance of relationships between variables, allowing for the discovery of patterns and insights that can inform decision-making.

5 Must Know Facts For Your Next Test

1. Support is calculated as the proportion of transactions in the dataset that contain a specific item or itemset, expressed as a percentage.
2. High support values indicate that an item or itemset is common within the dataset, making it potentially more significant for analysis.
3. In practice, support helps filter out less interesting patterns that occur infrequently, allowing analysts to focus on more relevant associations.
4. Support plays a key role in determining candidate itemsets during the process of generating association rules in market basket analysis.
5. Setting an appropriate minimum support threshold is essential, as it affects the number of rules generated and their overall relevance in practical applications.

Review Questions

• How does support help in identifying relevant patterns in a dataset?
  • Support helps identify relevant patterns by quantifying how frequently a specific item or combination of items occurs in the dataset. A higher support value suggests that the pattern is more prevalent, making it more likely to be significant for analysis. This frequency-based approach allows analysts to prioritize their focus on stronger associations, ultimately leading to better insights and decision-making.
• Discuss the relationship between support and confidence in the context of association rule mining.
  • Support and confidence are closely related metrics in association rule mining. While support measures how often an itemset appears in the dataset, confidence indicates the likelihood that a rule is true given its support. Together, they help assess the strength and reliability of associations. A high support value coupled with high confidence suggests a strong relationship between items, making it valuable for deriving actionable insights.
• Evaluate the importance of setting a minimum support threshold when performing market basket analysis.
  • Setting a minimum support threshold is crucial in market basket analysis as it directly impacts the number of rules generated and their practical relevance. A low threshold may result in too many insignificant rules that don't provide actionable insights, while a high threshold could overlook important associations. Balancing this threshold is key to ensuring that the resulting patterns are both frequent enough to be meaningful and relevant enough to drive business decisions.

Related terms

Confidence:

A measure of the likelihood that a rule is true given its support, indicating how often items in a dataset appear together.

Association Rule Mining: A data mining technique used to discover interesting relationships and patterns among items in large datasets.

Lift:

A metric used to evaluate the effectiveness of an association rule, showing how much more likely two items are to be bought together compared to being bought independently.