Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

Abstract

In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks—usually referred to as self-organizing maps (SOMs)—to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from conventional SOMs, Growing SOMs (GSOMs), Growing Grid Networks (GGNs), Incremental Grid Growing (IGG) approach, Growing Neural Gas (GNG) method as well as our two original solutions, i.e., Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1DN also referred to as Dynamic SOMs (DSOMs)) and Generalized SOMs with Tree-Like Structures (GeSOMs with T-LSs) are discussed. They are characterized in terms of (i) the modification mechanisms used, (ii) the range of network modifications introduced, (iii) the structure regularity, and (iv) the data-visualization/data-clustering effectiveness. The performance of particular solutions is illustrated and compared by means of selected data sets. We also show that the proposed original solutions, i.e., GeSOMs with 1DN (DSOMs) and GeSOMS with T-LSs outperform alternative approaches in various complex clustering tasks by providing up to 20 % increase in the clustering accuracy. The contribution of this work is threefold. First, algorithm-oriented original computer-implementations of particular SOM’s generalizations are developed. Second, their detailed simulation results are presented and discussed. Third, the advantages of our earlier-mentioned original solutions are demonstrated.

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

“…Unsupervised learning does not require data to be labelled and there are no notions of the output during the learning process [34]. A characteristic of majority of unsupervised learning-based techniques is relatively high computational complexity [35]. Since Kohonen's introduction of the method in early 1980s, great progress has been made developing the structure and learning algorithm, resulting in many effective applications, among which there are also highly complex ones [35].…”

“…However, in the application of the method, we made proper adjustments to the research approach to perfectly address our main goal. Also, most studies that are reporting the vitality of the method relate to core SOMs [35]. Some applications have been in the broad financial field, however, the research scope, and especially the goals, differs considerably from studying the banking sector, e.g., [38][39][40], up to financial studies of the corporate sector [41].…”