Abstract
Distributed Object-oriented software has been used in a large number of applications for solving complex problems in different scientific fields like: machine learning, data mining, pattern recognition, image analysis and bioinformatics. However, we need to collect objects into groups such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. This collection is called cluster analysis or clustering. Each cluster will be assigned to different computer to minimize communication among objects and speedup the execution of tasks. There have been several clustering techniques applied to objects. In this paper, we introduce three algorithms for clustering objects into present numbers of clusters to match the target hardware (i.e. software restructuring). These algorithms, through simulation results, achieve better performance than the existing algorithms as they generate more accurate clusters in less time.