Data Mining Research by Iryna Ivchenko
This chapter presents a hierarchical taxonomy of supervised learning methods, organized along three primary dimensions: algorithmic architecture, learning mechanism, and model interpretability. We trace the evolutionary development from early statistical classifiers through decision tree families, neural architectures, kernel methods, and ensemble strategies. Special attention is given to the i...
The proliferation of data mining techniques over the past three decades has created an urgent need for systematic organization and classification of methodological approaches. This chapter establishes a comprehensive meta-taxonomic framework for understanding, categorizing, and relating the diverse landscape of data mining methods. We propose a three-dimensional classification scheme that organ...
This chapter chronicles the remarkable metamorphosis of data mining techniques across four transformative decades, from the pioneering expert systems of the 1960s to the sophisticated ensemble methods and standardized methodologies of the early 2000s. We trace the intellectual lineage from DENDRAL's rule-based reasoning through Quinlan's revolutionary decision tree algorithms, the renaissance o...
This chapter chronicles the revolutionary transformation of data mining during the big data era, spanning from Google's MapReduce paradigm in 2004 to the present age of intelligent, automated mining systems. We examine how the confluence of distributed computing, deep learning, and cloud infrastructure fundamentally redefined both the scale and sophistication of knowledge discovery from data. T...
This chapter traces the fascinating journey of data mining from its embryonic roots in 19th-century statistics to its crystallization as a formal discipline in the 1990s. We explore how Francis Galton's pioneering work on regression analysis and Karl Pearson's correlation coefficients laid the mathematical groundwork for pattern discovery. The narrative advances through the computational revolu...