The performance of resultant topological structure of Kohonen Self Organizing Map SOM is highly dependent of the learning rate and neighborhood parameters. In literature there are plenty many different types of approaches to and proposals for those parameters. It has been investigated that in general the learning rate and neighborhood parameters are data independent and predefined before the training period. Here in this paper a novel approach has been proposed to change the learning rate parameter according to the interaction of neurons with data. During training, the worst matching neuron also tracked and used to trace the formation of topological structure of SOM. A slight modification on conventional learning rate with proposed method has a considerable influence on resultant topologies in a positive way. The effects of this approach has been tested with the real world problem and different synthetic data.