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The Partitioning Around Medoids (PAM) implementation of the K-Medoids algorithm in Python [Unmaintained]

License: MIT License

Python 8.58% Jupyter Notebook 91.42%
machine-learning kmedoids-clustering medoids partitioning cluster clusters unsupervised-learning

kmedoids's Introduction

K-Medoids

K-Medoids is a clustering algorithm. Partitioning Around Medoids (PAM) algorithm is one such implementation of K-Medoids

Prerequisites

  • Scipy
  • Numpy

Getting Started

from KMedoids import KMedoids

Parameters

  • n_cluster: number of clusters
  • max_iter: maximum number of iterations
  • tol: tolerance level

Example

Wiki Example

data = [[2, 6], [3, 4], [3, 8], [4, 7], [6, 2], [6, 4], [7, 3], [7, 4], [8, 5], [7, 6]]
k_medoids = KMedoids(n_cluster=2)
k_medoids.fit(data)

Visualization

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