/fuzzycmeans

A fast implementation of fuzzy c-means clustering algorithm using numpy

Primary LanguagePythonMIT LicenseMIT

fuzzycmeans

A fast implementation of fuzzy c-means clustering algorithm using numpy.

The implementation was based on the paper: "Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing, BaiQing Sun, Haifeng Guo, Hamid Reza Karimi, Yuanjing Ge, Shan Xiong".

How to use

  • First install the numpy library and clone the repository.
  • Import cmeans.py
  • call cmeans function

Parameters:

  • data: must be a numpy array
  • n_c: number of clusteres
  • m: fuzzyness coefficient
  • epsilon: minimum error
  • max_steps: max steps to finish the execution