1. A method of computer data analysis using neural networks, the method including: generating a data representation using a data set, the data set including a plurality of attributes wherein generating the data representation includes: modifying the data set using a training algorithm, wherein the training algorithm includes growing the data set and wherein growing the data set includes: finding K q for each of the data set nodes, where K q is the node with the highest average quantization error, arg ⁢ ⁢ max q ⁢ ⁢ { q _ ⁡ ( t ) K q } for each of the data set nodes, where q _ ⁡ ( t ) K q = 1 t - 1 ⁢ ∑ t = 1 t = t - 1 ⁢ q ⁡ ( t ) K q is the average quantization error for node q, where: K x = arg ⁢ ⁢ max x ⁢ {  K q - K < r ⁡ ( q ) - 1 , c ⁡ ( q ) >  ,  K q - K < r ⁡ ( q ) + 1 , c ⁡ ( q ) >  } K y = arg ⁢ ⁢ max y ⁢ {  K q - K < r ⁡ ( q ) , c ⁡ ( q ) - 1 >  ,  K q - K < r ⁡ ( q ) , c ⁡ ( q ) + 1 >  } if ∥K y −K c <∥K x −K c ∥ then n r =r(y) if r(y)<r(c), else n r =r(c); and n c =c(y) else n r =r(y); n c =c(x) if c(x) c(c), else n c =c(c); inserting a new row and column after row n r and column n c ; and interpolating new attribute values for the newly inserted node vectors using: K < r , n c > = ( K < r , n c - 1 > + K < r , c n + 1 > ) ⁢ α 2 ⁢ ⁢ and K < n r , c > = ( K < n r - 1 , c > + K < n r + 1 , c > ) ⁢ α 2 , where aεU(0,1). performing convergence testing, wherein convergence testing checks for convergence of the training algorithm; repeating the modification of the data set until convergence of the training algorithm occurs; and displaying one or more subsets of the data set using the data representation.