An object-oriented implementation of bacteria foraging system for data clustering application

In resent time social Foraging behavior of common bacteria like E.Coli is simulated for the solution of complex global optimization problems and this is known as Bacteria Foraging Optimization Algorithm (BFOA). BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains. This paper describes an object-oriented implementation of the Bacteria Foraging System (BFS) for the solution of data clustering problem. In data clustering it divides data into meaningful groups called clusters without any prior knowledge, and it is the key technique in data mining and has become an important issue in a number of fields. We have tested our implementation for number artificial dataset and some common benchmarks, also we have compared the results with other popular clustering methods named K-means algorithm, GA based technique, BCO and PSO-based algorithms, and experimental results show the effectiveness of proposed implementation.