Visualization of hidden structures in corporate failure prediction using opposite pheromone per node model

The oppositional and antipodal forms of forces, entities and quantities have been envisaged in the context of practical and applied field of engineering and management science in order to create a more complete picture of reality. The interplay between entities and opposite entities is apparently fundamental for maintaining universal balance. A large number of problems in engineering and science cannot be approached with conventional schemes and are generally handled with intelligent techniques such as evolutionary, neural, reinforcing and swarm-based methods. Visualization of unforeseen financial events is one of those applications, where failure of particular corporate firm can be forecasted based on the combination of several indicators. The hidden artifacts of corporate financial events could also be evaluated with the help of ant-based behavior of pheromone deposition. The learning in the pheromone deposition is subjected to oppositional forces leading towards the equilibrium of the corporate of interest. The motivation of this paper is to initiate the model to analyze and to represent the financial practices of typical clusters, which may cause failure of that firm in near future. In this paper we propose to use opposition based learning and Soft Bergman Based Clustering to implement the proposed model. Brief comparison of results is presented at the end of the proposal.