š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Chapter 14: Grand Conclusion ā The Future of Intelligent Data Analysis. Intellectual Data Analysis Series. Stabilarity Research Hub, ONPU. DOI: 10.5281/zenodo.14910147 By Iryna Ivchenko & Oleh Ivchenko | Stabilarity Hub | February 2026 Opening Reflection: The Journey from Discovery to Intelligence In 1989, when Gregory Piatetsky-Shapiro…
Category: Intellectual Data Analysis
Data Mining Research by Iryna Ivchenko
Chapter 13: Emerging Frontiers in Data Mining (2024-2026)
By Iryna Ivchenko & Oleh Ivchenko | Stabilarity Hub | February 2026 Opening Narrative: The Acceleration In October 2024, a team at Google DeepMind published AlphaFold 3, demonstrating protein structure prediction capabilities that surpassed experimental methods in both accuracy and speed. What made this achievement remarkable was not merely the scientific breakthrough but the methodology:…
Chapter 12: Cross-Domain Synthesis ā Universal Patterns in Data Mining
š Academic Citation: Ivchenko, I., & Ivchenko, O. (2026). Cross-Domain Synthesis: Universal Patterns in Data Mining. Intellectual Data Analysis Series. Stabilarity Research Hub, ONPU. DOI: Pending Zenodo registration Opening Narrative: The Universal Patterns In 2023, a team of researchers at MIT made a striking observation: the same algorithmic patterns used to predict credit card fraud…
Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Density-Based Clustering: DBSCAN, OPTICS, and the Taxonomy of Shape-Aware Grouping. Intellectual Data Analysis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18701939 Abstract Density-based clustering methods represent a fundamentally different philosophy of grouping than their partitional and hierarchical counterparts: rather than minimizing geometric distances or optimizing variance, they…
Hierarchical Clustering Taxonomy: From Dendrograms to Modern Extensions
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Hierarchical Clustering Taxonomy: From Dendrograms to Modern Extensions. Intellectual Data Analysis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18683667 Abstract Hierarchical clustering represents one of the oldest and most intuitive approaches to unsupervised pattern discovery ā the idea that natural structures in data can be revealed through…
Chapter 9: Clustering and Segmentation ā Grouping Strategies in Data Mining
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Clustering and Segmentation ā Grouping Strategies in Data Mining. Intellectual Data Analysis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18672455 Abstract Clustering stands at the heart of unsupervised data mining ā it is the art of asking a machine to look at raw, unlabeled data and answer…
Chapter 8: Sequential Pattern Mining ā Temporal Discoveries
š Academic Citation: Iryna Ivchenko & Oleh Ivchenko. (2026). Chapter 8: Sequential Pattern Mining ā Temporal Discoveries. Intellectual Data Analysis Series, Chapter 8. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18666030 Abstract Sequential pattern mining represents one of the most intellectually compelling challenges in data mining: discovering meaningful patterns hidden within the temporal dimension of data. Unlike…
Data Mining Chapter 7: Association Rule Mining ā Discovering Relationships
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Association Rule Mining ā Discovering Relationships. Intellectual Data Analysis Series, Chapter 7. Odesa National Polytechnic University. Opening Narrative: The Beer and Diapers Legend In the early 1990s, a rumor began circulating through the corridors of data mining conferences that would become the field’s most enduring urban…
Data Mining Chapter 6: Unsupervised Learning Taxonomy ā Pattern Discovery Without Labels
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Data Mining Chapter 6: Unsupervised Learning Taxonomy ā Pattern Discovery Without Labels. Intellectual Data Analysis Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18648774 Abstract This chapter develops a systematic taxonomy of unsupervised learning methods for data mining applications. We classify approaches across four major paradigms: clustering algorithms…
Data Mining Chapter 5: Supervised Learning Taxonomy ā Classification and Regression
š Academic Citation: Ivchenko, I. & Ivchenko, O. (2026). Supervised Learning Taxonomy: Classification Methods and Research Gaps. Data Mining: A Taxonomic Framework ā Chapter 5. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18626630 Abstract This chapter presents a hierarchical taxonomy of supervised learning methods, organized along three primary dimensions: algorithmic architecture, learning mechanism, and model interpretability. We…