MULTIDIMENSIONAL AND TIME-BASED ASSOCIATION PATTERN EXTRACTION: AN EXTENSIVE REVIEW OF CONTEMPORARY METHODS AND OBSTACLES

Abstract: In multivariable and time-based data analysis, Association Rule Mining (ARM) is crucial because it helps to discover new structures and significant associations that have not been observed earlier. Built on top of the aforementioned challenges, classical Association Rule Mining (ARM) methods were designed to operate on a static, dedicated operational data repository and thus have substantial limitations for dynamic, dispersed, diffuse, and multi- attribute data systems. We provide a review of the state of the art on multivariate, time-varying relationship patterns from 1997 to 2024. The research examines multiple methods, such as OLAP-based data extraction, fuzzy association pattern retrieval, decentralised extraction structure, multifeatured frequent pattern creation, time-based pattern extraction, real- time training methods, GAN-based sequential extraction, and a group-enhanced fuzzy time-based extraction framework. Moreover, a relative examination of this reviewed method is conducted based on expandability, processing-based difficulty, time-based flexibility and extraction performance. The review also recognised significant study gaps, including pattern repetition, expandability constraints, elevated numerical burden, and real-time computation challenges. Ultimately, upcoming study paths, including smart, flexible extraction, the incorporation of advanced training, and an expandable, decentralised, time-based extraction structure, are discussed. The review provides a brief analytical summary of contemporary methods for extracting correlation patterns and their implementations in smart multivariable information assessment frameworks.

Keywords: Association Rule Mining, Time-based Mining, Multivariable Data Mining, Frequent Pattern Extraction, Fuzzy Association Laws, Distributed Extraction.