Classification Techniques in Data Mining: A Comprehensive Study

Data mining classification techniques become increasingly relevant because it is sensible to bring meaningful patterns out from very large data sets; however, cross-comparison and comparison in terms of different domains, in terms of several classifiers that exist, is discussed inadequately. A systematic review of the five prime classification algorithms has been made for a large set of varied datasets that range from healthcare and finance domains up to social media domains. Employed a newly developed multi-metric evaluation approach, which included accuracy, F1-score, computation performance, and model explainability. With the use of the 15 datasets to evaluate 50,000 instances, ensemble algorithms—namely Random Forest and XGBoost—were found to perform steadily better than isolated base learning classifiers, achieving a respective average of 94.2 percent in contrast to 87.6 percent for isolated techniques. In addition, DNN appeared to perform outstandingly on high-dimensional features but was computationally 3.5 times more complex compared to the tree-based technique. The study also found an enormous trade-off between model complexity and interpretability: the simpler models retained 85% of the performance while presenting better explainability. Such findings show empirical evidence for selecting optimum classification techniques on domain requirements and computational constraints, further adding towards decision-making in real-life data-mining applications.