Data mining is the process of analyzing enormous data and summarizing it into the useful knowledge discovery and the task of data mining approaches is growing quickly, particularly classification techniques very efficient, way to classifying the data, which is important in the decision-making process for medical practitioners. This study presents the quantization and validation (OQV) techniques for fast outlier detection in large size WDBC data sets. The distance metrics utilization makes the algorithm as the linear one for various objects and assures the sequential scanning. The inclusion of direct quantization technique and the cluster explicit discovery assures the simplicity and the economical. The comparative analysis of proposed OQV techniques with the triangular boundary-based classification and the Weighing-based Feature Selection and Monotonic Classification (WFSMC) regarding the accuracy, precision, recall and the number of attributes assures an effectiveness of OQV for large size datasets.
Learning rule sets and Sugeno integrals for monotonic classification problems
