LIONbook Chapter 14: Self-organizing maps
The LIONbook on machine learning and optimization, written by co-founders of LionSolver software, is provided free for personal and non-profit usage. Chapter 14 looks at Self-organizing maps.
Here is the latest chapter from LIONbook, a new book dedicated to "LION" combination of Machine Learning and Intelligent Optimization, written by the developers of LionSolver software, Roberto Battiti and Mauro Brunato.
This book is freely available on the web.
Here are the previous chapters:
- Chapters 1-2: Introduction and nearest neighbors.
- Chapter 3: Learning requires a method
- Chapter 4: Linear models
- Chapter 5: Mastering generalized linear least-squares
- Chapter 6: Rules, decision trees, and forests
- Chapter 7: Ranking and selecting features
- Chapter 8: Specific nonlinear models
- Chapter 9: Neural networks, shallow and deep
- Chapter 10: Statistical Learning Theory and Support Vector Machines (SVM).
- Chapter 11: Democracy in machine learning: how to combine different methods.
- Chapter 12: Top-down clustering: K-means.
- Chapter 13: Bottom-up (agglomerative) clustering.
You can also download the entire book here.
The latest chapter is Chapter 14: Self-organizing maps.
From the previous chapters, you are now familiar with the basic clustering techniques. Clustering identifies group of similar data, in some cases with a hierarchical structure (groups, then groups containing groups, ...). If an internal representation is available, a group can be represented with a prototype. This chapter deals with prototypes arranged according to a regular grid-like structure and influencing each other if they are neighbors in this grid.
The idea is to cluster data (entities) while at the same time visualizing this clustered structure on a two-dimensional map. One wants a visualization that is at least approximately coherent with the clustering This should be puzzling enough to continue reading.