Deep Self-Organizing Maps for Visual Data Mining

Visual data mining facilitates the involvement of domain experts in the data mining processes. The effectiveness of visual data mining is especially dominant when paired with unsupervised methods due to the abundance of unlabeled data. Deep Self-Organizing Maps (DSOMs) are unsupervised learning architectures capable of high level feature abstraction. In this paper, we analyze the effectiveness of using DSOMs for visual data mining. DSOM's visual data mining capability was evaluated using the following visual data explorations methodologies: 1) U-Matrix, 2) hit maps and 3) data histograms. In comparison with traditional single layered SOM architectures, experimental results showed that DSOMs produced more accurate visual representations of the underlying data distributions. Therefore, DSOM is a viable method for generating easily understandable visual representations of high-dimensional complex datasets. These visual representations can be powerful tools in the real world, leading to better understanding of systems and thus enabling the design of better algorithms for control and monitoring.