new OPTICS(matrix, epsilon, min_points, metricopt) → {OPTICS}
Ordering Points To Identify the Clustering Structure.
Parameters:
| Name | Type | Attributes | Default | Description |
|---|---|---|---|---|
matrix | Matrix | the data. | ||
epsilon | Number | the minimum distance which defines whether a point is a neighbor or not. | ||
min_points | Number | the minimum number of points which a point needs to create a cluster. (Should be higher than 1, else each point creates a cluster.) | ||
metric | function | <optional> | euclidean | the distance metric which defines the distance between two points of the |
- Source
- See
- To Do
- needs restructuring.
Returns:
- Type:
- OPTICS
Methods
get_cluster_affirmation() → {Array}
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Returns:
Returns an array, where the ith entry defines the cluster affirmation of the ith point of matrix. (-1 stands for outlier)
- Type:
- Array
get_clusters() → {Array.<Array>}
Returns an array of clusters.
- Source
Returns:
Array of clusters with the indices of the rows in given matrix.
- Type:
- Array.<Array>
init()
Computes the clustering.
- Source