AbstractThis paper deals with methods for clustering of continuous signals such as time series data sets. Centers of classes are determined with the help of the neural network with process input, which is an extension of the traditional artificial neural network into the time domain. Collaborative Artificial Bee Colony algorithm is based on the search of food behaviour of honey bees for training in a non-trajectory way. An Enhancement has been done to the original Artificial Bee Colony (ABC) algorithm and was used to discover suitable domain specific architectures. The C-ABC has great explorative search features and better convergence compared to the original algorithm and it was proved empirically that it avoids local minima by promoting exploration of the search space. In SPNN(Self Organizing Process Neural Network), the inputs and weights are related to instantaneous conditions. The proposed algorithm results in clustering the data sets with reduced error rate and better convergence rate. The tests are conducted on empirical data in matlab.