Available Partitioning Methods

Enums

enum class PartitioningMethod

Available methods for partitioning a hierarchy into flat clusters.

Values:

enumerator K

Partition into a fixed number of clusters \(k\).

**Parameters:**
- `k` (default: `2`): Number of clusters the data should be partitioned into.

enumerator Elbow

Use the Elbow method to determine the optimal number of clusters.

enumerator Threshold

Cut the hierarchy at a fixed similarity threshold.

**Parameters:**
- `k` (default: `2`): Number of clusters the data should be partitioned into.

enumerator ThresholdElbow

Combine threshold cutting and the Elbow method for more adaptive partitioning.

enumerator QCoverage

Use \(q\)-coverage to ensure that a given proportion of mass is retained in the clustering.

**Parameters:**
- `k` (default: `2`): Number of clusters the data should be partitioned into.
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

enumerator QCoverageElbow

Combine \(q\)-coverage and the Elbow method for adaptive and coverage-aware partitioning.

**Parameters:**
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

enumerator QStem

Use the \(q\)-stem criterion to extract partitions based on internal branch structure.

**Parameters:**
- `k` (default: `2`): Number of clusters the data should be partitioned into.
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

enumerator QStemElbow

Combine \(q\)-stem with Elbow-based refinement for improved robustness.

**Parameters:**
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

enumerator LcaNoiseElbow

Elbow method that incorporates noise suppression using least common ancestors (LCA).

enumerator LcaNoiseElbowNoTriangle

Noise-aware Elbow method that excludes triangular merging to improve sharpness.

enumerator MedianOfElbows

Take the median of multiple Elbow criteria to find a more stable clustering.

**Parameters:**
- `elbow_start_z` (default: `1`): Range start to use for elbow method.
- `elbow_end_z` (default: `5`): Range end to use for elbow method.

enumerator MeanOfElbows

Take the mean of multiple Elbow criteria to smooth over noise in the hierarchy.

**Parameters:**
- `elbow_start_z` (default: `1`): Range start to use for elbow method.
- `elbow_end_z` (default: `5`): Range end to use for elbow method.

enumerator Stability

Optimize cluster stability across multiple resolutions to find a robust partitioning, see also HDBSCAN.

**Parameters:**
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

enumerator NormalizedStability

Use normalized cluster stability to allow comparisons across hierarchies of different sizes.

**Parameters:**
- `min_cluster_size` (default: `5`): Minimum number of points which a cluster should contain.

Functions

std::string partitioning_method_to_string(PartitioningMethod type)

PartitioningMethod string_to_partitioning_method(const std::string &type)

std::vector<PartitioningMethod> get_available_partitioning_methods()

std::vector<std::string> get_available_partitioning_methods_as_strings()

Variables

constexpr std::array<std::pair<PartitioningMethod, std::string_view>, 14> partitioningMethodStrings{{{PartitioningMethod::K, "K"}, {PartitioningMethod::Elbow, "Elbow"}, {PartitioningMethod::Threshold, "Threshold"}, {PartitioningMethod::ThresholdElbow, "ThresholdElbow"}, {PartitioningMethod::QCoverage, "QCoverage"}, {PartitioningMethod::QCoverageElbow, "QCoverageElbow"}, {PartitioningMethod::QStem, "QStem"}, {PartitioningMethod::QStemElbow, "QStemElbow"}, {PartitioningMethod::LcaNoiseElbow, "LcaNoiseElbow"}, {PartitioningMethod::LcaNoiseElbowNoTriangle, "LcaNoiseElbowNoTriangle"}, {PartitioningMethod::MedianOfElbows, "MedianOfElbows"}, {PartitioningMethod::MeanOfElbows, "MeanOfElbows"}, {PartitioningMethod::Stability, "Stability"}, {PartitioningMethod::NormalizedStability, "NormalizedStability"},}}