Clustering

Clustering - Dbscan_FromFloatArray

Groups input points around clusters using DBSCAN algorithm.

IN

Name	Type	Description
InData	FloatArray	Data used for clustering （a single feature per sample）.
InMinPoints	Int	Minimum number of neighbors to create a cluster from a point. Range: 〔1, +inf）.
InEpsilon	Float	Maximum distance between two points to be considered neighbors. Range: （0, +inf）.
InIgnoreOutliers	Bool	If true, no errors are thrown if outliers are found.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）. A label set to -1 indicates an outlier.
Error	ErrorState	Gets the execution error message

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Clustering - Dbscan_FromFloatArrays

Groups input points around clusters using DBSCAN algorithm.

IN

Name	Type	Description
InData	FloatArrayArray	Data used for clustering （multiple features accepted per sample）.
InMinPoints	Int	Minimum number of neighbors to create a cluster from a point. Range: 〔1, +inf）.
InEpsilon	Float	Maximum distance between two points to be considered neighbors. Range: （0, +inf）.
InIgnoreOutliers	Bool	If true, no errors are thrown if outliers are found.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）. A label set to -1 indicates an outlier.
Error	ErrorState	Gets the execution error message

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Clustering - Dbscan_FromPoint2DArray

Groups input points around clusters using DBSCAN algorithm.

IN

Name	Type	Description
InPoints	Point2DArray	Points used for clustering.
InMinPoints	Int	Minimum number of neighbors to create a cluster from a point. Range: 〔1, +inf）.
InEpsilon	Float	Maximum distance between two points to be considered neighbors. Range: （0, +inf）.
InIgnoreOutliers	Bool	If true, no errors are thrown if outliers are found.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）. A label set to -1 indicates an outlier.
Error	ErrorState	Gets the execution error message

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Clustering - KMeans_FromFloatArray

Groups input samples around clusters using K-Means algorithm.

IN

Name	Type	Description
InData	FloatArray	Data used for clustering （a single feature per sample）.
InClusters	Int	Number of clusters in which to split the set. Range: 〔2, +inf）.
InMaxIterations	Int	Maximum number of iterations. Range: 〔1, +inf）.
InAttempts	Int	Number of times the algorithm is executed using different initial labels. The algorithm returns at the end the labels that yield the best compactness. Range: 〔1, +inf）.
InClustersInitialization	Enum < KMeansInitialization >	Method used to initialize clusters' centroids.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）.
Error	ErrorState	Gets the execution error message

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Clustering - KMeans_FromFloatArrays

Groups input samples around clusters using K-Means algorithm.

IN

Name	Type	Description
InData	FloatArrayArray	Data used for clustering （multiple features accepted per sample）.
InClusters	Int	Number of clusters in which to split the set. Range: 〔2, +inf）.
InMaxIterations	Int	Maximum number of iterations. Range: 〔1, +inf）.
InAttempts	Int	Number of times the algorithm is executed using different initial labels. The algorithm returns at the end the labels that yield the best compactness. Range: 〔1, +inf）.
InClustersInitialization	Enum < KMeansInitialization >	Method used to initialize clusters' centroids.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）.
Error	ErrorState	Gets the execution error message

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Clustering - KMeans_FromPoint2DArray

Groups input points around clusters using K-Means algorithm.

IN

Name	Type	Description
InPoints	Point2DArray	Points used for clustering.
InClusters	Int	Number of clusters in which to split the set. Range: 〔2, +inf）.
InMaxIterations	Int	Maximum number of iterations. Range: 〔1, +inf）.
InAttempts	Int	Number of times the algorithm is executed using different initial labels. The algorithm returns at the end the labels that yield the best compactness. Range: 〔1, +inf）.
InClustersInitialization	Enum < KMeansInitialization >	Method used to initialize clusters' centroids.
Name	String	Set the tool's name
Enable	Bool	Sets if current tool is enabled or not

OUT

Name	Type	Description
OutInt	IntArray	Labels assigned to each sample （indices starting from 0）.
Error	ErrorState	Gets the execution error message

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