Abstract

We classify clustering algorithms into sequence-based techniques---which transform the object net into a linear sequence---and partition-based clustering algorithms. Tsangaris and Naughton [TN91, TN92] have shown that the partition-based techniques are superior. However, their work is based on a single partitioning algorithm, the Kernighan and Lin heuristics, which is not applicable to realistically large object bases because of its high running-time complexity. The contribution of this paper is two-fold: (1) we devise a new class of greedy object graph partitioning algorithms (GGP) whose running-time complexity is moderate while still yielding good quality results. For large object graphs GGP is the best known heuristics with an acceptable running-time. (2) We carry out an extensive quantitative analysis of all well-known partitioning algorithms for clustering object graphs. Our analysis yields that no one algorithm performs superior for all object net characteristics. Therefore, we d...

Citations