Abstract

We present a divide-and-merge methodology for clustering a set of objects that combines a top-down “divide ” phase with a bottom-up “merge ” phase. In contrast, previous algorithms use either top-down or bottom-up methods for constructing a hierarchical clustering or produce a flat clustering using local search (e.g. k-means). Our divide phase produces a tree whose leaves are the elements of the set. For this phase, we suggest an efficient spectral algorithm. The merge phase quickly finds the optimal partition that respects the tree for many natural objective functions, e.g., k-means, min-diameter, min-sum, correlation clustering, etc. We present a metasearch engine that clusters results from web searches. We also give empirical results on textbased data where the algorithm performs better than or competitively with existing clustering algorithms. 1

Citations