Association analysis involves identifying relationships between variables within datasets. Association analysis is essential for various applications, including market basket analysis, recommendation systems, and fraud detection. By leveraging this technique, you can gain deeper insights into your data, enabling more strategic decision-making and ultimately driving better business outcomes.
- Correlation Analysis
- Pearson Correlation: Measures the linear relationship between two continuous variables, indicating how changes in one variable are associated with changes in another.
- Spearman’s Rank Correlation: Assesses how well the relationship between two variables can be described using a monotonic function, useful for ordinal data.
- Kendall’s Tau: Evaluates the ordinal association between two measured quantities, particularly useful for small datasets or when there are many tied ranks.
- Pattern Mining
- Sequential Pattern Mining: Identifies frequent sequences of events or behaviours.
- Closed Pattern Mining: Finds frequent item sets not part of larger frequent item sets.
- Maximal Pattern Mining: Discovers the largest frequent item sets.