Join GitHub today
GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.Sign up
Implementation and evaluation of multidimensional extension for k-means algorithm http://www.youngkwon.org/
Fetching latest commit…
Cannot retrieve the latest commit at this time.
|Type||Name||Latest commit message||Commit time|
|Failed to load latest commit information.|
1. Requirements: Program was developed and tested under Python 3.6.2 :: Anaconda, Inc. 2. How to execute: To run subKMeans with wine dataset use following command: python main.py To use subKMeans with arbitrary dataset (as 2D array named X) in Python code use (similar to sklearn.kmeans): subkmeans = SubKMeans(n_clusters=3).fit(X) #clustered labels are accessible through subkmeans.labels_ 3. The description of each source file: Utilities.py File containing supplementary function rvs() for generating random orthogonal matrices SubKMeans.py File containing source code of algorithm. Most functions has short descriptions. main.py File containing simple examples of dataset reading and clustering. 4. The operating system, where program was tested: Windows 10 Home 5. Additionally ---