K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python
-
Updated
Sep 7, 2017 - Python
K-Means++ Clustering using Gap Statistic for determining optimal value of K in Python
Repository for my master thesis content - Bag Of features for text detection in natural scene images
An implementation of the gap statistic, a method for estimating the number of clusters in a set of data.
Code library for common machine learning algorithms
Development of new ML library
K-means as an unsupervised machine learning technique. Customer Segmentation Case.
CLUST: clustering platform frontend
Estimating the number of clusters in a data set via the gap statistic. Implemented in H2O-3
Density Estimation, HMM Signature Verification, K-Means and Gap Statistics, Random Forests
Modified gap statistic (gap-com) for regularization selection of sparse networks. This method is aimed for complex network estimation.
Supervised and unsupervised analysis
Add a description, image, and links to the gap-statistic topic page so that developers can more easily learn about it.
To associate your repository with the gap-statistic topic, visit your repo's landing page and select "manage topics."