CA Algorithm for Optimized Interval Partitioning of Numerical Attributes

The CA algorithm effectively partitions numerical attributes into optimized intervals by integrating the advantages of both hierarchical clustering and partition-based clustering approaches. For varying initial cluster counts, this algorithm dynamically adjusts the number of classes during iteration. Classes with poor competitiveness - those with cardinality below a specified threshold - are progressively eliminated. The final output yields an optimized number of clusters that accurately reflects the actual data distribution pattern, making it particularly suitable for preprocessing continuous variables in machine learning pipelines through automated binning procedures.