ksigma

ksigma is a Python library designed for working with k-means clustering. It provides an efficient and user-friendly implementation of the k-means algorithm, a popular unsupervised machine learning technique used to partition a dataset into a specified number of clusters. The library aims to simplify the process of applying k-means clustering to various data analysis tasks.

The core functionality of ksigma revolves around the k-means clustering algorithm itself. This algorithm iteratively assigns data points to clusters based on their proximity to cluster centroids and then recalculates the centroids based on the mean of the assigned data points. ksigma offers parameters to control aspects of the clustering process, such as the number of clusters (k) and the maximum number of iterations. It can handle different types of numerical data and is often used for tasks like customer segmentation, image compression, and anomaly detection.

ksigma provides tools for initializing centroids, assigning data points, and updating centroids, making the underlying algorithm transparent to the user. The library is written in Python and integrates well with other scientific computing libraries in the Python ecosystem, such as NumPy and SciPy. This allows for seamless data manipulation and analysis before and after applying the k-means algorithm. Users can typically import ksigma and then instantiate a clustering object, fit it to their data, and retrieve the cluster assignments and centroids.