class HierClustering extends Clusterer with Error
Cluster several vectors/points using hierarchical clustering. Start with each point forming its own cluster and merge clusters until there are only 'k'.
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def
calcCentroids(): Unit
Calculate the centroids based on current assignment of points to clusters.
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def
classify(y: VectorD): Int
Given a new point/vector y, determine which cluster it belongs to.
Given a new point/vector y, determine which cluster it belongs to.
- y
the vector to classify
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- HierClustering → Clusterer
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clone(): AnyRef
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def
clustDist(setA: Set[VectorD], setB: Set[VectorD]): Double
Create initial clusters where each point forms its own cluster.
Create initial clusters where each point forms its own cluster.
- setA
the first set
- setB
the second set
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def
cluster(): Array[Int]
Iteratively merge clusters until until the number of clusters equals k.
Iteratively merge clusters until until the number of clusters equals k.
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- HierClustering → Clusterer
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def
distance(u: VectorD, v: VectorD): Double
Compute a distance metric between vectors/points u and v.
Compute a distance metric between vectors/points u and v.
- u
the first vector/point
- v
the second vector/point
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eq(arg0: AnyRef): Boolean
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def
finalClusters(): Unit
For each data point, determine its cluster assignment.
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def
finalize(): Unit
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def
flaw(method: String, message: String): Unit
Show the flaw by printing the error message.
Show the flaw by printing the error message.
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the method where the error occurred
- message
the error message
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def
getClass(): Class[_]
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def
getName(i: Int): String
Get the name of the i-th cluster.
Get the name of the i-th cluster.
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def
hashCode(): Int
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def
initClusters(): Unit
Create initial clusters where each point forms its own cluster.
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final
def
isInstanceOf[T0]: Boolean
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def
name_(n: Array[String]): Unit
Set the names for the clusters.
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wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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