zhipeng93 commented on code in PR #110:
URL: https://github.com/apache/flink-ml/pull/110#discussion_r898854960
##########
flink-ml-lib/src/main/java/org/apache/flink/ml/clustering/kmeans/KMeans.java:
##########
@@ -153,39 +149,24 @@ public IterationBodyResult process(
DataStream<Integer> terminationCriteria =
centroids.flatMap(new TerminateOnMaxIter(maxIterationNum));
- DataStream<Tuple2<Integer, DenseVector>> centroidIdAndPoints =
+ DataStream<Tuple2<Integer[], DenseVector[]>> centroidIdAndPoints =
points.connect(centroids.broadcast())
.transform(
- "SelectNearestCentroid",
+ "CentroidsUpdateAccumulator",
new TupleTypeInfo<>(
- BasicTypeInfo.INT_TYPE_INFO,
- DenseVectorTypeInfo.INSTANCE),
- new
SelectNearestCentroidOperator(distanceMeasure));
+
BasicArrayTypeInfo.INT_ARRAY_TYPE_INFO,
+ ObjectArrayTypeInfo.getInfoFor(
+
DenseVectorTypeInfo.INSTANCE)),
+ new
CentroidsUpdateAccumulator(distanceMeasure));
Review Comment:
One optimization from algorithm side could be pre-compute the norm of each
data point and thus accelerate the computing of `findClosestCentroidId`.
A similar solution could be found [1] [2].
[1]
https://github.com/alibaba/Alink/blob/9ae07329a9fb77e39c334c09c79e5016b62f59ee/core/src/main/java/com/alibaba/alink/operator/common/distance/FastDistance.java
[2]
https://github.com/apache/spark/blob/2fa98a6a0971cafeb98a2148a962cf8d79381842/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L222
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