Self-organizing data mining commences ideology of progression – legacy, transformation and assortment – for engendering a system of configuration methodically by facilitating mechanical copy arrangement amalgamation and replica corroboration. Models are produced in an adaptive manner from data, in shape of configuration of energetic neurons in an evolutionary manner by repetitively generating populations of challenging models of mounting complication, which may lead to corroboration and assortment in anticipation of a most advantageous multifaceted model.

The data collected leads to the growth of a tree-like network that comes out of the seed of information which have been segregated into input and output variables’ used in the development of the data. The growth takes place in an evolutionary method by the amalgamation of a pair of neurons and the neuron that overcomes the other leads to the development of a new combination. While this process goes on neither the quantity of neurons nor the quantity of stratum in the system are pre-determined. Neither it is possible to predefine the actual behaviour of the newly developed neurons.

All this is regulated at the point in the procedure of self-organization, and for that reason, it is called self-organizing data mining. This is a powerful tool but can be used easily to extract the data by analysing the knowledge gather while collecting it.