Semi-supervised learning techniques: k-means clustering in OODB fragmentation

Vertical and horizontal fragmentations are central issues in the design process of distributed object based systems. A good fragmentation scheme followed by an optimal allocation could greatly enhance performance in such systems, as data transfer between distributed sites is minimized. In this paper we present a horizontal fragmentation approach that uses the k-means AI clustering method for partitioning object instances into fragments. Our new method applies to existing databases, where statistics are already present. We model fragmentation input data in a vector space and give different object similarity measures together with their geometrical interpretations. We provide quality and performance evaluations using a partition evaluator function