Tag Archives: Cluster

Clustering by fast search and find of density peaks

This post is about a new cluster algorithm published by Alex Rodriguez and Alessandro Laio in the latest Science magazine. The method is short and efficient, I implemented it using about only 100 lines of cpp code. BASIC METHOD There are two leading criteria in this method: Local Density and Minimum Distance with higher density.  Rho above is the […]

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K-Means Clustering

ABOUT UNSUPERVISED LEARNING In supervised learning problems, we deal with labeled data, means during the training process, we give our machine both Xs and Ys, by training, we ‘forge’ a system, which given new Xs, it is able to guess the output Ys. By using better algorithm and sophisticated methods, we can make the forged system […]

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