srowen commented on a change in pull request #27758: [SPARK-31007][ML][WIP]
KMeans optimization based on triangle-inequality
URL: https://github.com/apache/spark/pull/27758#discussion_r408157153
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File path:
mllib/src/main/scala/org/apache/spark/mllib/clustering/DistanceMeasure.scala
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@@ -17,23 +17,124 @@
package org.apache.spark.mllib.clustering
+import org.apache.spark.SparkContext
import org.apache.spark.annotation.Since
+import org.apache.spark.broadcast.Broadcast
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.linalg.BLAS.{axpy, dot, scal}
import org.apache.spark.mllib.util.MLUtils
private[spark] abstract class DistanceMeasure extends Serializable {
+ /**
+ * Statistics used in triangle inequality to obtain useful bounds to find
closest centers.
+ * @param distance distance between two centers
+ */
+ def computeStatistics(distance: Double): Double
+
+ /**
+ * Statistics used in triangle inequality to obtain useful bounds to find
closest centers.
+ *
+ * @return A symmetric matrix containing statistics, matrix(i)(j) represents:
Review comment:
It might be too hard to implement, but if it's symmetric you only need half
this many elements to represent. The indexing becomes more complex though
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