Study on Classification Algorithms for Multi-relational Data Mining

Abstract

Data Mining (DM) constitutes a fundamental stage in the Knowledge Discovery in Databases (KDD) process, emphasizing the systematic analysis of large-scale datasets to uncover meaningful patterns, trends, and relationships. As data continues to grow in volume and complexity, the application of advanced analytical techniques has become essential for transforming raw data into actionable knowledge. DM employs a range of methods to address analytical challenges, including classification algorithms, association rule mining, and neural network approaches. This study investigates the effectiveness of various classification algorithms namely: ID3, C4.5, J48 and the general Decision Tree methodology in solving classification problems within data mining tasks. A benchmark dataset from the UCI Machine Learning Repository is utilized to illustrate the practical application of these algorithms. The WEKA software tool is employed for data preprocessing, model development, and performance evaluation through metrics such as accuracy and predictive power. The experimental results highlight the capability of classification techniques to categorize data points efficiently and extract valuable insights. Overall, the study underscores the critical role of classification-based data mining techniques in enhancing knowledge discovery and supporting informed decision-making across diverse domains.