  We present in this paper three dynamic clustering techniques for
Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical &
Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage
statistics and the inter-object reference graph. They are quite elaborate.
However, they are also complex to implement and induce a high overhead. The
third clustering technique, called Detection & Reclustering of Objects (DRO),
is based on the same principles, but is much simpler to implement. These three
clustering algorithm have been implemented in the Texas persistent object store
and compared in terms of clustering efficiency (i.e., overall performance
increase) and overhead using the Object Clustering Benchmark (OCB). The results
obtained showed that DRO induced a lighter overhead while still achieving
better overall performance.
