Description

Bisecting k-means is a kind of hierarchical clustering algorithm.

Bisecting k-means algorithm starts from a single cluster that contains all points. Iteratively it finds divisible clusters on the bottom level and bisects each of them using k-means, until there are k leaf clusters in total or no leaf clusters are divisible.

Parameters

Name Description Type Required？ Default Value
minDivisibleClusterSize Minimum divisible cluster size Integer 1
k Number of clusters. Integer 4
distanceType Distance type for clustering, support EUCLIDEAN and COSINE. String “EUCLIDEAN”
vectorCol Name of a vector column String ✓
maxIter Maximum iterations, The default value is 10 Integer 10

Script Example

Code

  1. import numpy as np
  2. import pandas as pd
  3. data = np.array([
  4.     [0, "0 0 0"],
  5.     [1, "0.1,0.1,0.1"],
  6.     [2, "0.2,0.2,0.2"],
  7.     [3, "9 9 9"],
  8.     [4, "9.1 9.1 9.1"],
  9.     [5, "9.2 9.2 9.2"]
  10. ])
  11. df = pd.DataFrame({"id": data[:, 0], "vec": data[:, 1]})
  12. inOp1 = BatchOperator.fromDataframe(df, schemaStr='id int, vec string')
  13. inOp2 = StreamOperator.fromDataframe(df, schemaStr='id int, vec string')
  14. kmeans = BisectingKMeansTrainBatchOp().setVectorCol("vec").setK(2)
  15. predictBatch = BisectingKMeansPredictBatchOp().setPredictionCol("pred")
  16. kmeans.linkFrom(inOp1)
  17. predictBatch.linkFrom(kmeans, inOp1)
  18. [model,predict] = collectToDataframes(kmeans, predictBatch)
  19. print(model)
  20. print(predict)
  22. predictStream = BisectingKMeansPredictStreamOp(kmeans).setPredictionCol("pred")
  23. predictStream.linkFrom(inOp2)
  24. predictStream.print(refreshInterval=-1)
  25. StreamOperator.execute()

Results

Model
  1. rowId   model_id                                         model_info
  2. 0         0  {"vectorCol":"\"vec\"","distanceType":"\"EUCLI...
  3. 1   1048576  {"clusterId":1,"size":6,"center":{"data":[4.6,...
  4. 2   2097152  {"clusterId":2,"size":3,"center":{"data":[0.1,...
  5. 3   3145728  {"clusterId":3,"size":3,"center":{"data":[9.1,...
Prediction
  1. rowId   id          vec  pred
  2. 0   0        0 0 0     0
  3. 1   1  0.1,0.1,0.1     0
  4. 2   2  0.2,0.2,0.2     0
  5. 3   3        9 9 9     1
  6. 4   4  9.1 9.1 9.1     1
  7. 5   5  9.2 9.2 9.2     1