How to optimal K in K – Means Algorithm [duplicate]

by

The base idea is to evaluate cluster scoring on sample data, usally it is distance inside cluster and distance between clusters. The more this measure the better clustering, based on this mesure you can select best clustring paramters. One of metrics can be found here http://alias-i.com/lingpipe/docs/api/com/aliasi/cluster/ClusterScore.html

More Related Contents:

  1. python kmeans on string
  2. Cluster analysis in R: determine the optimal number of clusters
  3. How do I determine k when using k-means clustering?
  4. Is it possible to specify your own distance function using scikit-learn K-Means Clustering?
  5. 1D Number Array Clustering
  6. scikit-learn DBSCAN memory usage
  7. Reading wav file in Java
  8. Speed-efficient classification in Matlab
  9. Matlab – PCA analysis and reconstruction of multi dimensional data
  10. Clustering text documents using scikit-learn kmeans in Python
  11. Finding 2 & 3 word Phrases Using R TM Package
  12. Can someone give an example of cosine similarity, in a very simple, graphical way? [closed]
  13. Why does one hot encoding improve machine learning performance? [closed]
  14. Cluster one-dimensional data optimally? [closed]
  15. Is Spark’s KMeans unable to handle bigdata?
  16. K-means algorithm variation with equal cluster size
  17. Clustering values by their proximity in python (machine learning?) [duplicate]
  18. What is the difference between linear regression and logistic regression? [closed]
  19. Simple approach to assigning clusters for new data after k-means clustering
  20. random unit vector in multi-dimensional space
  21. Mixing categorial and continuous data in Naive Bayes classifier using scikit-learn
  22. Group n points in k clusters of equal size [duplicate]

Leave a Comment