InEMMiner : An Incremental Learning Model for Exceptional Model Mining Using Optimized Deep Learning

ABSTRACT Data mining is a technique that involves evaluating big data in order to reveal patterns or connections that can aid in solving business problems through the analysis of data. Exceptional model mining (EMM) is a system for finding local patterns in a dataset by identifying subgroups whose behavior deviates substantially from that which is anticipated. Incremental learning allows the model to continuously learn from new examples while retaining prior knowledge. In the instance of EMM, incremental learning increases the process by adding new training samples, which helps to enhance the model's performance over time. This research presents an incremental learning approach in EMMiner (InEMMiner) using a deep learning model. A novel hybrid optimizing algorithm, double exponential‐running city game optimizer (DE‐RCGO), is proposed by putting together double exponential smoothing (DES) and running city game optimizer (RCGO) for the purpose of finding outstanding subgroups in within‐individual clusters. To assess quality of these subgroups, four different metrics are suggested: (1) a combination of weighted Kraskov entropy with a support vector machine (SVM), (2) a combination of weighted Fuzzy entropy with an Autoencoder, (3) a combination of weighted Boltzmann entropy with a gated recurrent unit (GRU), and (4) a metric that combines weighted Gibbs entropy with isolation forest. Such measures are incorporated in the EMMiner system in order to effectively filter and prioritize most appropriate exceptional subgroups with an improvement on overall EMM task performance. InEMMiner incorporates an incremental learning approach using a deep learning model, TabNet, which is further optimized by the proposed DE‐RCGO algorithm. Experimental results validate the efficiency of the model, with quality metric achieving a value of 98.9%, memory usage of 985.65 MB, and a computational time of 81.52 s.