## 4 Conclusions  
In this paper, we briefly present several modifications and generalizations of the concept of SOMs in order to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from the 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 presented. Original computer-implementations of particular solutions are developed and their detailed simulation results are shown. The performance of particular solutions is illustrated and compared by means of selected data sets. Table 1 summarizes the characteristics of the considered solutions 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. Our GeSOMs with T-LSs have been effectively used in complex clustering tasks including the clustering of huge amounts of gene expression data [15; 17; 18], electricity consumption data [19], and WWW documents [16]. Our future work aims at equipping our both approaches (in particular, GeSOMs with T-LSs) with data visualization mechanisms while maintaining their full capacity for automatic and precise clustering of data. We hope that such a generalization contributes to bridging two SOM-related research areas of dimensionality-reduction-based data visualization and complex data clustering. \begin{table} \begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} \\ \hline \multirow{4}{*}{**GSOMs**} & \multirow{4}{*}{**Methods**} & Adding neurons & no & yes & yes & yes & yes & yes & yes \\  & & Removing neurons & no & no & no & yes & yes & yes & yes \\  & & Adding connections & no & no & no & yes & yes & yes & yes \\  & & Removing connections & no & no & no & yes & yes & yes & yes \\ \hline \multirow{4}{*}{**GSOMs**} & \multirow{4}{*}{**Methods**} & Increasing the size & no & yes & yes & yes & yes & yes \\  & & Reducing the size & no & no & no & yes & yes & yes \\  & & Disconnection into subnetworks & no & no & no & yes & yes & yes \\  & & Reconnection of some of & & no & no & n/r*1 & n/r*1 & yes & yes \\  & & subnetworks & & & & & & & \\ \hline \multirow{4}{*}{**GSOMs**} & \multirow{4}{*}{**Methods**} & Fully connected & yes & yes & yes & no & no & no \\  & & Regular, rectangular structure & yes & yes & yes & yes & no & no & no \\ \hline \multicolumn{2}{c}{**GSOMs**} & \multicolumn{1}{c}{**GSOMs**} & Form of data visualization & \(U_{j}\) (4) & \(U_{j}\) (4) & \(U_{j}\) (4) & - & \(H_{j}^{dist}\) (5) & - \\ \hline \multirow{4}{*}{**GSOMs**} & \multirow{4}{*}{**Methods**} & data visualization & \multirow{4}{*}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} \\ \cline{1-1}  & & data clustering & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Methods**} \\ \hline \hline \end{tabular}   
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**Author Contributions:** Conceptualization, M.B.G. and F.R.