Name
Abstract | ||
|---|---|---|
A clustering algorithm partitions a set of data points into smaller sets (clusters) such that each subset is more tightly packed than the whole. Many approaches to clustering translate the vector data into a graph with edges reflecting a distance or similarity metric on the points, then look for highly connected subgraphs. We introduce such an algorithm based on ideas borrowed from the topological notion of thin position for knots and 3-dimensional manifolds. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10711-013-9848-z | Geometriae Dedicata |
Keywords | Field | DocType |
Thin position,Data mining,Graph partitioning,68R10,57M20 | Fuzzy clustering,Topology,Strength of a graph,Discrete mathematics,Combinatorics,Correlation clustering,Graph embedding,Connected component,Cluster analysis,Topological graph theory,Mathematics,Topological graph | Journal |
Volume | Issue | ISSN |
abs/1206.0771 | 1 | 0046-5755 |
Citations | PageRank | References |
2 | 0.46 | 2 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesse Johnson | 1 | 4 | 1.89 |