Name
Abstract | ||
|---|---|---|
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 reorderings. We show these can be accomplished efficiently, by exploiting special properties of squared Euclidean distances and by using techniques from scheduling algorithms. |
Year | Venue | Keywords |
|---|---|---|
2003 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 16 | hill climbing,hierarchical clustering,cost function,euclidean distance |
Field | DocType | Volume |
Hierarchical clustering,Mathematical optimization,Square (algebra),Scheduling (computing),Computer science,Hierarchical clustering of networks,Artificial intelligence,Euclidean geometry,Brown clustering,Machine learning,Single-linkage clustering | Conference | 16 |
ISSN | Citations | PageRank |
1049-5258 | 2 | 0.46 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Kauchak | 1 | 363 | 25.92 |
| Sanjoy Dasgupta | 2 | 2052 | 172.00 |