# Cluster embeddings¶

`HDBSCAN`

Identify clusters using the distance between provided embeddings.

Eqivalent to `cluster_dataset`

, but instead of a dataset expects a column of embeddings as input. The input may e.g.
be word2vec embeddings from an `embed_text`

step, or whole dataset embeddings from an `embed_dataset`

step.

Optionally reduces the dimensionality of the embeddings (by default using UMAP). This may help with making the data denser (counteracting the "curse-of-dimensionality"), and thus making it potentially easier to identify clusters.

The clustering algorithm used by default is (HDBSCAN), which produces a column of positive cluster IDs, or -1 if a data point is considered noise (not belonging to any cluster).

For further detail on HDBSCAN's parameters see its documentation here (for usage) and here (for its API).

#### Example¶

The following configuration applies clustering with the default values:

```
cluster_embeddings(ds, {
"algorithm": "hdbscan",
"min_cluster_size": 120,
"min_samples": 15,
"reduce": {
"weights": None,
"weights_max": 32,
"weights_exp": 2,
"algorithm": "umap",
"n_components": 10,
"n_neighbors": 100,
"min_dist": 0,
"random_state": 42,
}) -> (ds.cluster)
```

## Usage¶

The following are the step's expected inputs and outputs and their specific types.

```
cluster_embeddings(embeddings: list[number], {"param": value}) -> (cluster: category)
```

where the object `{"param": value}`

is optional in most cases and if present may contain any of the parameters described in the
corresponding section below.

### Inputs¶

embeddings: column:list[number]

### Outputs¶

cluster: column:category

Column containing cluster tags.

### Parameters¶

For further detail on HDBSCAN's parameters see its documentation here (for usage) and here (for its API).

metric: string = "euclidean"

The metric used to calculate similarity between data points.

Must be one of:
`"euclidean"`

,
`"manhattan"`

,
`"chebyshev"`

,
`"minkowski"`

,
`"canberra"`

,
`"braycurtis"`

,
`"haversine"`

,
`"mahalanobis"`

,
`"wminkowski"`

,
`"seuclidean"`

,
`"cosine"`

,
`"correlation"`

,
`"hamming"`

,
`"jaccard"`

,
`"dice"`

,
`"russellrao"`

,
`"kulsinski"`

,
`"rogerstanimoto"`

,
`"sokalmichener"`

,
`"sokalsneath"`

,
`"yule"`

algorithm: string = "hdbscan"

Algorithm to use. The name of a supported clustering algorithm (currently allows `"hdbscan"`

only).

Must be one of:
`"hdbscan"`

min_cluster_size: integer = 120

Minimum cluster size. The minimum size for considering a region of dense data points a proper cluster.

Range: `1 ≤ min_cluster_size < inf`

min_samples: integer = 15

The larger the value, the more conservative the clustering. More points will be declared as noise, and clusters will be restricted to progressively more dense areas.

Range: `1 ≤ min_samples < inf`

reduce: object | null

Umap configuration. See more here. Params for dimensionality reduction.

## Items in `reduce`

algorithm: string = "umap"

Algorithm. The name of a supported dimensionality reduction algorithm.

Must be one of:
`"umap"`

encode_features: boolean = True

Preprocess features to normalize relative distances for quantitative and categorical variables.

weights: object | null

Weights used to multiply the normalized columns/features after vectorization. Should be a dictionary/object of `{"column_name": weight, ...}`

items. Will be scaled using the parameters
`weights_max`

, and `weights_exp`

before being applied. So only the relative weight of the columns is
important here, not their absolute values.

## Items in `weights`

column_weight: number

A `"column_name": numeric_weight`

pair. Each column name must refer to an existing column in the dataset.

Example parameter values:

`{"date": 0.5, "age": 2}`

type_weights: object | null

Weights used to multiply the normalized columns/features after vectorization. Should be a dictionary/object of `"type": weight"`

items. Will be scaled using the parameters
`weights_max`

, and `weights_exp`

before being applied. So only the relative weight of the columns is
important here, not their absolute values.

## Items in `type_weights`

number: number

Weight for columns of type `Number`

datetime: number

Weight for columns of type `Datetime`

category: number

Weight for columns of type `Category`

ordinal: number

Weight for columns of type `Ordinal`

embedding: number

Weight for columns of type `Embedding`

(`List[Number]`

).

multilabel: number

Weight for columns of type `Multilabel`

(`List[Category]`

).

weights_max: number = 32

Maximum weight to scale the normalized columns with.

Range: `0 ≤ weights_max < inf`

weights_exp: integer = 2

Weight exponent. Weights will be raised to this power before(!) scaling to `weights_max`

. This allows for a non-linear mapping from input
weights to those used eventually to multiply the normalized columns.

n_neighbors: integer = 100

Number of neighbours. Use smaller numbers to concentrate on the local structure in the data, and larger values to focus on the more global structure.

For further details see here.

Range: `1 ≤ n_neighbors < inf`

min_dist: number = 0.1

Minimum distance between reduced data points. Controls how tightly UMAP is allowed to pack points together in the reduced space. Smaller values will lead to points more tightly packed together (potentially useful if result is used to cluster the points). Larger values will distribute points with more space between them (which may be desirable for visualization, or to focus more on the global structure of the date).

For further details see here.

Range: `0 ≤ min_dist < inf`

n_components: integer = 10

Dimensionality of the reduced data.

Range: `1 ≤ n_components < inf`

metric: string = "euclidean"

Metric to use for measuring similarity between data points.

Must be one of:
`"euclidean"`

,
`"manhattan"`

,
`"chebyshev"`

,
`"minkowski"`

,
`"canberra"`

,
`"braycurtis"`

,
`"haversine"`

,
`"mahalanobis"`

,
`"wminkowski"`

,
`"seuclidean"`

,
`"cosine"`

,
`"correlation"`

,
`"hamming"`

,
`"jaccard"`

,
`"dice"`

,
`"russellrao"`

,
`"kulsinski"`

,
`"rogerstanimoto"`

,
`"sokalmichener"`

,
`"sokalsneath"`

,
`"yule"`

n_epochs: integer | null

Number of training iterations used in optimizing the embedding. Larger values result in more accurate embeddings. If `null`

is specified a value will be selected based on the size of the input dataset
(200 for large datasets, 500 for small).

init: string = "spectral"

How to initialize the low dimensional embedding. When ‘spectral’, uses a spectral embedding; when‘ random’ assigns initial embedding positions at random.

Must be one of:
`"spectral"`

,
`"random"`

low_memory: boolean = False

Avoid excessive memory use. For some datasets nearest neighbor computations can consume a lot of memory. If you find the step is failing due to memory constraints,
consider setting this option to `true`

. This approach is more computationally expensive, but avoids excessive memory use.

target: string | null

Target variable (labels) for supervised dimensionality reduction. Name of the column that contains your target values (labels).

target_weight: number = 0.5

Weighting factor between features and target. A value of 0.0 weights entirely on data, and a value of 1.0 weights entirely on target. The default of 0.5 balances the weighting equally between data and target.

random_state: integer | null = 42

A random number to initialize the algorithm for reproducibility.