Partitions the graph into clusters using k-means clustering.
Remarks
The nodes of the graph will be partitioned into k clusters, based on their positions such that their distance to the cluster's mean (centroid) is minimized.
Other Clustering Algorithms
yFiles for HTML supports a number of other clustering algorithms:
- HierarchicalClustering – partitions the graph into clusters by merging smaller clusters based on their distance.
- EdgeBetweennessClustering – partitions the graph into clusters based on edge-betweenness centrality.
- BiconnectedComponentClustering – partitions the graph into clusters based on its biconnected components.
- LouvainModularityClustering – partitions the graph into clusters by applying the Louvain modularity method.
- LabelPropagationClustering – partitions the graph into clusters by applying a label propagation algorithm.
Examples
// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering()
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId))
}// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering()
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId)!)
}Type Details
- yfiles module
- view-layout-bridge
- yfiles-umd modules
- view-layout-bridge
- Legacy UMD name
- yfiles.analysis.KMeansClustering
See Also
k or if no centroids are given, random initial centroids will be assigned for all clusters.Constructors
Creates a new instance of this class.
Parameters
A map of options to pass to the method.
- metric - DistanceMetric
- k - number
The number of clusters. This option sets the k property on the created object.
- maximumIterations - number
The maximum number of iterations performed by the algorithm for convergence. This option sets the maximumIterations property on the created object.
- centroids - IEnumerable<Point>
The initial centroids. This option sets the centroids property on the created object.
- subgraphNodes - ItemCollection<INode>
The collection of nodes which define a subset of the graph for the algorithms to work on. This option sets the subgraphNodes property on the created object.
- subgraphEdges - ItemCollection<IEdge>
The collection of edges which define a subset of the graph for the algorithms to work on. This option sets the subgraphEdges property on the created object.
Properties
Gets or sets the initial centroids.
Gets or sets the number of clusters.
Remarks
1.Gets or sets the collection of edges which define a subset of the graph for the algorithms to work on.
Remarks
If nothing is set, all edges of the graph will be processed.
If only the excludes are set all edges in the graph except those provided in the excludes are processed.
Note that edges which start or end at nodes which are not in the subgraphNodes are automatically not considered by the algorithm.
ItemCollection<T> instances may be shared among algorithm instances and will be (re-)evaluated upon (re-)execution of the algorithm.
Examples
// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering({
// Ignore edges without target arrow heads
subgraphEdges: {
excludes: (edge) =>
edge.style instanceof PolylineEdgeStyle &&
edge.style.targetArrow === IArrow.NONE
}
})
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId))
}// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering({
// Ignore edges without target arrow heads
subgraphEdges: {
excludes: (edge: IEdge): boolean =>
edge.style instanceof PolylineEdgeStyle &&
edge.style.targetArrow === IArrow.NONE
}
})
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId)!)
}Gets or sets the collection of nodes which define a subset of the graph for the algorithms to work on.
Remarks
If nothing is set, all nodes of the graph will be processed.
If only the excludes are set all nodes in the graph except those provided in the excludes are processed.
ItemCollection<T> instances may be shared among algorithm instances and will be (re-)evaluated upon (re-)execution of the algorithm.
Examples
// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering({
subgraphNodes: {
// only consider elliptical nodes in the graph
includes: (node) =>
node.style instanceof ShapeNodeStyle &&
node.style.shape === ShapeNodeShape.ELLIPSE,
// but ignore the first node, regardless of its shape
excludes: graph.nodes.first()
}
})
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId))
}// prepare the k-means clustering algorithm
const algorithm = new KMeansClustering({
subgraphNodes: {
// only consider elliptical nodes in the graph
includes: (node: INode): boolean =>
node.style instanceof ShapeNodeStyle &&
node.style.shape === ShapeNodeShape.ELLIPSE,
// but ignore the first node, regardless of its shape
excludes: graph.nodes.first()
}
})
// run the algorithm
const result = algorithm.run(graph)
// highlight the nodes of the clusters with different styles
for (const node of graph.nodes) {
const componentId = result.nodeClusterIds.get(node)
graph.setStyle(node, clusterStyles.get(componentId)!)
}Methods
Partitions the graph into clusters using k-means clustering.
Complexity
O(|V| ⋅ k ⋅ I) where k is the
Parameters
A map of options to pass to the method.
- graph - IGraph
- The input graph to run the algorithm on.
Returns
- ↪KMeansClusteringResult
- The resulting (non-empty) clusters
Throws
- Exception({ name: 'InvalidOperationError' })
- If the algorithm can't create a valid result due to an invalid graph structure or wrongly configured properties.
k or if no centroids are given, random initial centroids will be assigned for all clusters.