Automatic Clustering Old and Reverse Engineered Databases by Evolutional Processing

Keeping of old databases is difficult. Especially when the documentaries of system are infirm or written documentaries are deleted. Reverse engineering methods are tried to improve and solving this problem. Various methods and conceptive models are presented for discovering and extraction old databases. Some of these methods are tried to do normalization up to different levels. But after normalization, perception conceptive of these databases and also the relations of system’s different parts are considered. So for improve, perception and managing these databases better, various methods are presented for their automatic clustering. This research presents an automatic algorithm for clustering of the relations of database tables by evolutional processing. This suggested method tries to optimize the time of execution of this clustering for managing the databases better.