| dc.description.abstract | Social insects (or animals) provide us with a powerful concept to create decentralized systems of simple interacting, and often mobile, agents (e.g. ants, birds, etc.) The study of their behaviors provides us with effective tools for solving many difficult problems such as optimization, etc. More and more researchers are interested in this exciting way of achieving a form of swarm intelligence (i.e. the emergent collective intelligence of groups of simple agents.) They have created computer simulations of various interpretations of the movement of organisms in a bird flock, fish school, or ant colonies.
In this paper, a new data visualization method, which was inspired by real birds behaviors, is proposed. In this method, each data pattern in the data set to be clustered is regarded as a piece of food and these data patterns will be sequentially tossed to a flock of birds on the ground. The flock of birds adjusts its physical movements to seek food. Individual members of the flock can profit from discoveries of all of other members of the flock during the search for food because an individual is influenced by the success of the best neighbor and tries to imitate the behavior of the best neighbor. Gradually, the flock of birds will be divided into several groups according to the distributions of the food. The formed groups will naturally correspond to the underlying data structures in the data set.
However many practical data sets are consisted of high-dimensional data points; therefore, how to generalize the aforementioned idea to cluster high-dimensional data sets is a very demanding challenge. Since the Self-Organizing Map (SOM) algorithm can project high-dimensional data points into a low-dimensional space through a self-organizing procedure we decide to integrate the SOM algorithm with the foregoing swarm intelligence to propose a new data visualization algorithm d. We then name the new data visualization algorithm as the Swarm Intelligence-based SOM (SISOM) algorithm. The algorithm allows us to use our visualization to decide the numbers of clusters and then cluster the data set based on the estimated cluster number. Nine data sets are used to demonstrate the effectiveness of the proposed algorithm. | en_US |