Design of An Incremental Q Learning Model for Improving Efficiency of Rule-Mining Based Automatic Clustering Architectures
Keywords:
Incremental Q Learning Model, Genetic Algorithm, Rule-mining, Automatic Clustering, Efficiency EnhancementsAbstract
Increasing the efficacy of rule-mining-based automatic clustering architectures is a prerequisite for many data-driven applications. In this paper, we present a novel method, the Incremental Q Learning Model (IQLM), which combines Q learning and the Genetic Algorithm for iterative optimizations. Our model addresses the need to improve clustering effectiveness by maximizing inter-class variance and minimizing intra-class variance levels. Existing methods frequently struggle to achieve optimal feature selection and parameter tuning, which can have a substantial impact on clustering performance under real-time scenarios. Our IQLM incorporates a Genetic Algorithm Model that performs feature selection, retaining high variance features from input datasets and samples, in order to circumvent these limitations. This method assists in identifying key discriminative characteristics, thereby enhancing the accuracy of clustering process. In our method, the Q Learning Model computes an Iterative Q Value that quantifies the ratio of Inter Cluster Centroid Variance to Intraclass Sample Variance levels. This Q Value is a dynamic measure of clustering effectiveness. The model optimizes the clustering parameters by iteratively adjusting the values of Minimum Support (for FPGrowth, Apriori, and FPMax), Epsilon, and Min Samples for DBSCAN. As a result, the Q Values are recalculated, and a reward function based on the estimated improvement is derived for different datasets & samples. The efficacy of our proposed model is demonstrated by empirical evaluations conducted on diverse data sets. In comparison to existing methods, the IQLM is 45% efficient in terms of precision, accuracy & recall levels. Our proposed IQLM's characteristics make it suitable for real-time scenarios. Its ability to dynamically modify clustering parameters and feature selection ensures adaptability in fluctuating data environments. The achieved efficiency enhancement improves the scalability and usability of the automatic clustering architecture, making it suitable for a wide range of data-driven applications in real-world environments.
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