k_means

Numpy implementation of k-means clustering algorithm.

Supports Pandas DataFrame and Numpy ndarrays as input format.

Parameters

• init(): initilaizing the K-means object with the number of clusters
  • k: int: number of clusters
• fit_predict(): compute cluster centers and predict cluster index for each sample
  • data: input data, can be a Pandas DataFrame or Numpy ndarray object
  • dist_type: str: type of distance measure to be used, can be euclidean (default), manhattan, chebychev
  • max_iter: int: number of maximum iterations, default is 20
• predict(): predict the closest cluster each sample in X belongs to
  • data: input data, can be a Pandas DataFrame or Numpy ndarray object

Attributes

• cluster_centers_: numpy ndarray: coordinates of cluster centers
• inertia_: float: sum of squared distances of samples to their closest cluster center

Usage:

from k_means.K_Means import K_Means

kmeans = K_Means(3)
labels, centroids, iterations = kmeans.fit_predict(data)

new_data = np.array([[-1,1],[0,-2],[2,0]])
kmeans.predict(new_data)

Examples