We describe a procedure which finds a hierarchical clustering by hill- climbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorder- ings. We show these can be accomplished efficiently, by exploiting spe- cial properties of squared Euclidean distances and by using techniques from scheduling algorithms.

We describe a procedure which finds a hierarchical clustering by hillclimbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorderings. We show these can be accomplished efficiently, by exploiting special properties of squared Euclidean distances and by using techniques from scheduling algorithms.

We describe a procedure which finds a hierarchical clustering by hillclimbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorderings. We show these can be accomplished efficiently, by exploiting special properties of squared Euclidean distances and by using techniques from scheduling algorithms.

We describe a procedure which finds a hierarchical clustering by hillclimbing. Thecost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorderings. Weshow these can be accomplished efficiently, by exploiting special properties of squared Euclidean distances and by using techniques from scheduling algorithms.