###### 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 1 DN 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 1 DN (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. artificial intelligence; computational intelligence; artificial neural networks; self-organizing neural networks; self-organizing maps; high-dimensional data visualization; complex data clustering +
Footnote †: journal:  
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e.g., \"a varied collection of studies that testify to the vitality of the field of self-organizing systems for data analysis. Most of them relate to the core models in the field, namely self-organizing maps (SOMs)\" (the quotation from Preface of the recently published [6]). The objective of this paper is to briefly present several modifications and generalizations of the concept of SOMs (including our two original solutions)--starting from the simplest and ending with the most advanced ones--in order to illustrate their advantages in applications ranging from high-dimensional data visualization to complex data clustering. After brief presentation of a conventional SOM, two approaches that are able to automatically increase the number of neurons in their networks are outlined. They include Growing SOM (GSOM) [7] and Growing Grid Network (GGN) [8]. Next, a solution additionally equipped with the ability to add or remove some topological connections in the network is briefly presented. It is Incremental Grid Growing (IGG) approach [9]. In turn, Growing Neural Gas (GGN) approach [10] that can also remove some neurons from the network is outlined. Finally, our two original solutions that are able to automatically adjust the number of neurons in the network (to \"grow\"/reduce its structure), to disconnect it into substructures as well as to reconnect some of them again are outlined. They include Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1 DN [11]) operating on splitting-merging neuron chains (they are also referred to as Dynamic SOMs (DSOMs) in [12; 13]) and Generalized SOMs with splitting-merging Tree-Like Structures (GeSOMs with T-LSs [14; 15; 16; 17; 18; 19]). The operation and performance of particular solutions are illustrated and compared by means of some data sets. 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.