Advanced Preprocessing Techniques for Transaction Data Analysis

The analysis of transaction data is often impeded by challenges such as noise, inconsistencies, high dimensionality, and missing values, which obscure valuable insights. Existing preprocessing techniques, while effective in isolated scenarios, often struggle to address the complexity and heterogeneity of large-scale transaction datasets, particularly when dealing with dynamic, highfrequency, and multi-source data. This paper presents a comprehensive preprocessing framework that integrates advanced techniques—such as data cleaning, transformation, feature engineering, and dimensionality reduction—to enhance data quality, consistency, and usability. Methods including Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and clustering algorithms are employed to address data quality issues and improve pattern recognition. Unlike previous approaches, the proposed framework systematically addresses both data inconsistency and scalability, demonstrating superior performance in enhancing clustering accuracy, identifying transaction trends, and revealing user behavior patterns. Experimental results confirm that our approach significantly improves data structure clarity, supporting more robust decision-making. These findings highlight the necessity of advanced preprocessing in transaction data analysis and lay the groundwork for future AI-driven methodologies aimed at improving data handling and analytical performance.