K-Means Clustering

This section describes the K-Means algorithm in the Neo4j Graph Data Science library.

1. Introduction

K-Means clustering is an unsupervised learning algorithm that is used to solve clustering problems. It follows a simple procedure of classifying a given data set into a number of clusters, defined by the parameter k. The clusters are then positioned as points and all observations or data points are associated with the nearest cluster, computed, adjusted and then the process starts over using the new adjustments until a desired result is reached.

For more information on this algorithm, see:

2. Syntax

K-Means syntax per mode
Run K-Means in stream mode on a named graph.
CALL gds.alpha.kmeans.stream(
  graphName: String,
  configuration: Map
)
YIELD
  nodeId: Integer,
  communityId: Integer
Table 1. Parameters
Name Type Default Optional Description

graphName

String

n/a

no

The name of a graph stored in the catalog.

configuration

Map

{}

yes

Configuration for algorithm-specifics and/or graph filtering.

Table 2. Configuration
Name Type Default Optional Description

nodeLabels

List of String

['*']

yes

Filter the named graph using the given node labels.

relationshipTypes

List of String

['*']

yes

Filter the named graph using the given relationship types.

concurrency

Integer

4

yes

The number of concurrent threads used for running the algorithm.

jobId

String

Generated internally

yes

An ID that can be provided to more easily track the algorithm’s progress.

nodeProperty

String

n/a

no

A node property to be used by the algorithm.

k

Integer

10

yes

Number of desired clusters.

maxIterations

Integer

10

yes

The maximum number of iterations of K-Means to run.

deltaThreshold

Float

0.05

yes

Value as a percentage to determine when to stop early. If fewer than 'deltaThreshold * |nodes|' nodes change their cluster , the algorithm stops. Value must be between 0 (exclusive) and 1 (inclusive).

randomSeed

Integer

n/a

yes

The seed value to control the initial centroid assignment.

Table 3. Results
Name Type Description

nodeId

Integer

Node ID.

communityId

Integer

The community ID.

Run K-Means in stats mode on a named graph.
CALL gds.alpha.kmeans.stats(
  graphName: String,
  configuration: Map
)
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  postProcessingMillis: Integer,
  communityDistribution: Map,
  configuration: Map
Table 4. Parameters
Name Type Default Optional Description

graphName

String

n/a

no

The name of a graph stored in the catalog.

configuration

Map

{}

yes

Configuration for algorithm-specifics and/or graph filtering.

Table 5. Configuration
Name Type Default Optional Description

nodeLabels

List of String

['*']

yes

Filter the named graph using the given node labels.

relationshipTypes

List of String

['*']

yes

Filter the named graph using the given relationship types.

concurrency

Integer

4

yes

The number of concurrent threads used for running the algorithm.

jobId

String

Generated internally

yes

An ID that can be provided to more easily track the algorithm’s progress.

nodeProperty

String

n/a

no

A node property to be used by the algorithm.

k

Integer

10

yes

Number of desired clusters.

maxIterations

Integer

10

yes

The maximum number of iterations of K-Means to run.

deltaThreshold

Float

0.05

yes

Value as a percentage to determine when to stop early. If fewer than 'deltaThreshold * |nodes|' nodes change their cluster , the algorithm stops. Value must be between 0 (exclusive) and 1 (inclusive).

randomSeed

Integer

n/a

yes

The seed value to control the initial centroid assignment.

Table 6. Results
Name Type Description

preProcessingMillis

Integer

Milliseconds for preprocessing the data.

computeMillis

Integer

Milliseconds for running the algorithm.

postProcessingMillis

Integer

Milliseconds for computing percentiles and community count.

communityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

Run K-Means in mutate mode on a named graph.
CALL gds.alpha.kmeans.mutate(
  graphName: String,
  configuration: Map
)
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  mutateMillis: Integer,
  postProcessingMillis: Integer,
  nodePropertiesWritten: Integer,
  communityDistribution: Map,
  configuration: Map
Table 7. Parameters
Name Type Default Optional Description

graphName

String

n/a

no

The name of a graph stored in the catalog.

configuration

Map

{}

yes

Configuration for algorithm-specifics and/or graph filtering.

Table 8. Configuration
Name Type Default Optional Description

mutateProperty

String

n/a

no

The node property in the GDS graph to which the cluster is written.

nodeLabels

List of String

['*']

yes

Filter the named graph using the given node labels.

relationshipTypes

List of String

['*']

yes

Filter the named graph using the given relationship types.

concurrency

Integer

4

yes

The number of concurrent threads used for running the algorithm.

jobId

String

Generated internally

yes

An ID that can be provided to more easily track the algorithm’s progress.

nodeProperty

String

n/a

no

A node property to be used by the algorithm.

k

Integer

10

yes

Number of desired clusters.

maxIterations

Integer

10

yes

The maximum number of iterations of K-Means to run.

deltaThreshold

Float

0.05

yes

Value as a percentage to determine when to stop early. If fewer than 'deltaThreshold * |nodes|' nodes change their cluster , the algorithm stops. Value must be between 0 (exclusive) and 1 (inclusive).

randomSeed

Integer

n/a

yes

The seed value to control the initial centroid assignment.

Table 9. Results
Name Type Description

preProcessingMillis

Integer

Milliseconds for preprocessing the data.

computeMillis

Integer

Milliseconds for running the algorithm.

mutateMillis

Integer

Milliseconds for adding properties to the projected graph.

postProcessingMillis

Integer

Milliseconds for computing percentiles and community count.

nodePropertiesWritten

Integer

Number of properties added to the projected graph.

communityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

Run K-Means in write mode on a named graph.
CALL gds.alpha.kmeans.write(
  graphName: String,
  configuration: Map
)
YIELD
  preProcessingMillis: Integer,
  computeMillis: Integer,
  writeMillis: Integer,
  postProcessingMillis: Integer,
  nodePropertiesWritten: Integer,
  communityDistribution: Map,
  configuration: Map
Table 10. Parameters
Name Type Default Optional Description

graphName

String

n/a

no

The name of a graph stored in the catalog.

configuration

Map

{}

yes

Configuration for algorithm-specifics and/or graph filtering.

Table 11. Configuration
Name Type Default Optional Description

nodeLabels

List of String

['*']

yes

Filter the named graph using the given node labels.

relationshipTypes

List of String

['*']

yes

Filter the named graph using the given relationship types.

concurrency

Integer

4

yes

The number of concurrent threads used for running the algorithm.

jobId

String

Generated internally

yes

An ID that can be provided to more easily track the algorithm’s progress.

writeConcurrency

Integer

value of 'concurrency'

yes

The number of concurrent threads used for writing the result to Neo4j.

writeProperty

String

n/a

no

The node property in the Neo4j database to which the cluster is written.

nodeProperty

String

n/a

no

A node property to be used by the algorithm.

k

Integer

10

yes

Number of desired clusters.

maxIterations

Integer

10

yes

The maximum number of iterations of K-Means to run.

deltaThreshold

Float

0.05

yes

Value as a percentage to determine when to stop early. If fewer than 'deltaThreshold * |nodes|' nodes change their cluster , the algorithm stops. Value must be between 0 (exclusive) and 1 (inclusive).

randomSeed

Integer

n/a

yes

The seed value to control the initial centroid assignment.

Table 12. Results
Name Type Description

preProcessingMillis

Integer

Milliseconds for preprocessing the data.

computeMillis

Integer

Milliseconds for running the algorithm.

writeMillis

Integer

Milliseconds for adding properties to the Neo4j database.

postProcessingMillis

Integer

Milliseconds for computing percentiles and community count.

nodePropertiesWritten

Integer

Number of properties added to the projected graph.

communityDistribution

Map

Map containing min, max, mean as well as p50, p75, p90, p95, p99 and p999 percentile values of community size for the last level.

configuration

Map

The configuration used for running the algorithm.

3. Examples

In this section we will show examples of running the K-Means algorithm on a concrete graph. The intention is to illustrate what the results look like and to provide a guide in how to make use of the algorithm in a real setting. We will do this on a small cities graph of a handful nodes connected in a particular pattern. The example graph looks like this:

Visualization of the example graph
The following Cypher statement will create the example graph in the Neo4j database:
CREATE
  (:City {name: 'Surbiton', coordinates: [51.39148, -0.29825]}),
  (:City {name: 'Liverpool', coordinates: [53.41058, -2.97794]}),
  (:City {name: 'Kingston upon Thames', coordinates: [51.41259, -0.2974]}),
  (:City {name: 'Sliven', coordinates: [42.68583, 26.32917]}),
  (:City {name: 'Solna', coordinates: [59.36004, 18.00086]}),
  (:City {name: 'Örkelljunga', coordinates: [56.28338, 13.27773]}),
  (:City {name: 'Malmö', coordinates: [55.60587, 13.00073]}),
  (:City {name: 'Xánthi', coordinates: [41.13488, 24.888]});

This graph is composed of various City nodes, in three global locations - United Kingdom, Sweden and the Balkan region in Europe.

We can now project the graph and store it in the graph catalog. We load the City node label with coordinates node property.

In the examples below we will use named graphs and native projections as the norm. However, Cypher projections can also be used.

The following statement will project the graph and store it in the graph catalog.
CALL gds.graph.project(
    'cities',
    {
      City: {
        properties: 'coordinates'
      }
    },
    '*'
)

In the following examples we will demonstrate using the K-Means algorithm on this graph to find communities of cities that are close to each other geographically.

3.1. Stream

In the stream execution mode, the algorithm returns the cluster for each node. This allows us to inspect the results directly or post-process them in Cypher without any side effects.

For more details on the stream mode in general, see Stream.

The following will run the algorithm and stream results:
CALL gds.alpha.kmeans.stream('cities', {
  nodeProperty: 'coordinates',
  k: 3,
  randomSeed: 42
})
YIELD nodeId, communityId
RETURN gds.util.asNode(nodeId).name AS name, communityId
ORDER BY communityId, name ASC
Table 13. Results
name communityId

"Kingston upon Thames"

0

"Liverpool"

0

"Surbiton"

0

"Sliven"

1

"Xánthi"

1

"Malmö"

2

"Solna"

2

"Örkelljunga"

2

In the example above we can see that the cities are geographically clustered together.

3.2. Stats

In the stats execution mode, the algorithm returns a single row containing a summary of the algorithm result. This execution mode does not have any side effects. It can be useful for evaluating algorithm performance by inspecting the computeMillis return item. In the examples below we will omit returning the timings. The full signature of the procedure can be found in the syntax section.

For more details on the stats mode in general, see Stats.

The following will run the algorithm and returns the result in form of statistical and measurement values
CALL gds.alpha.kmeans.stats('cities', {
  nodeProperty: 'coordinates',
  k: 3,
  randomSeed: 42
})
YIELD communityDistribution
Table 14. Results
communityDistribution

{ "p99": 3, "min": 2, "max": 3, "mean": 2.6666666666666665, "p90": 3, "p50": 3, "p999": 3, "p95": 3, "p75": 3 }

3.3. Mutate

The mutate execution mode extends the stats mode with an important side effect: updating the named graph with a new node property containing the cluster for that node. The name of the new property is specified using the mandatory configuration parameter mutateProperty. The result is a single summary row, similar to stats, but with some additional metrics. The mutate mode is especially useful when multiple algorithms are used in conjunction.

For more details on the mutate mode in general, see Mutate.

The following will run the algorithm and store the results in cities graph:
CALL gds.alpha.kmeans.mutate('cities', {
  nodeProperty: 'coordinates',
  k: 3,
  randomSeed: 42,
  mutateProperty: 'kmeans'
})
YIELD communityDistribution
Table 15. Results
communityDistribution

{ "p99": 3, "min": 2, "max": 3, "mean": 2.6666666666666665, "p90": 3, "p50": 3, "p999": 3, "p95": 3, "p75": 3 }

In mutate mode, only a single row is returned by the procedure. The result is written to the GDS in-memory graph instead of the Neo4j database.

3.4. Write

The write execution mode extends the stats mode with an important side effect: writing the cluster for each node as a property to the Neo4j database. The name of the new property is specified using the mandatory configuration parameter writeProperty. The result is a single summary row, similar to stats, but with some additional metrics. The write mode enables directly persisting the results to the database.

For more details on the write mode in general, see Write.

The following will run the algorithm and write the results back to Neo4j:
CALL gds.alpha.kmeans.write('cities', {
  nodeProperty: 'coordinates',
  k: 3,
  randomSeed: 42,
  writeProperty: 'kmeans'
})
YIELD nodePropertiesWritten
Table 16. Results
nodePropertiesWritten

8

In write mode, only a single row is returned by the procedure. The result is written to the Neo4j database instead of the GDS in-memory graph.