Journal of Theoretical and Applied Information Technology
In this paper, the authors propose a new learning method for clustering heterogeneous data with continuous class. This method in a first step finds the optimal path between the data using ant colony algorithms. The distance adopted in their optimization method takes into account all types of data. In the second step, instances in the optimal path are divided into homogeneous groups. A new criterion for the separation of clusters is used; it is based on transition probabilities between the instances. A third step is to find the prototype of each cluster to identify the cluster membership of any new data injected. After applying a clustering algorithm, they want to know whether the cluster structure found is valid or not.