A computational framework for exploratory data analysis.
Axel Wismüller · The European Symposium on Artificial Neural Networks · 2009
We introduce the Exploration Machine (Exploratory Ob- servation Machine, XOM) as a novel versatile method for the analysis of multidimensional data. XOM systematically inverts structural and func- tional components of so-called topology-preserving mappings. It provides a surprising flexibility to simultaneously contribute to complementary do- mains of unsupervised learning for exploratory pattern analysis, namely both structure-preserving dimensionality reduction and data clustering. We demonstrate XOM's applicability to synthetic and real-world data.