Table 5: Comparison of existing machine learning techniques with VFDT.

Features VFDT-CVFDT OVFDT EVFDT

Detection accuracy Very low Very low Good; does not handle outliers Excellent

Resource usage (time/memory) Less time; more memory More time in building two trees; requires additional memory Less time; less memory Having same time as VFDT but consuming very less memory space

Noisy data handling Does not handle noisy data Not appropriate under noisy data HB fluctuation intensifies under noisy data; accuracy decreases Handles noisy data efficiently

Tree size/pruning Small tree size; no pruning Same tree size as VFDT; no pruning Small tree size; incremental pruning Small tree size; iterative pruning

Computational resources Consuming less resources Consuming more resources by maintaining two trees Consuming less resources Consuming very less resources by cutting of HB outliers