Book, Data Science, KDD and Testing

Density-Based Clustering

Domino Data Lab

DECEMBER 2, 2020

Due to its importance in both theory and applications, this algorithm is one of three algorithms awarded the Test of Time Award at the KDD conference in 2014. Rather it infers the number of clusters based on the data, and it can discover clusters of arbitrary shape (for comparison, k-means usually discovers spherical clusters).

Metrics

Metrics KDD Testing Machine Learning

Using Empirical Bayes to approximate posteriors for large "black box" estimators

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Empirical Bayes methods find a prior such that when we add Poisson noise, we fit the distribution of our observed data. For an introduction to Empirical Bayes, see the paper [3] by Brad Efron (with more in his book [4]). Figure 4 shows the results of such a test. How exactly should we model $G$?

KDD

KDD Testing Machine Learning Modeling

Data Leaders Brief

Density-Based Clustering

Using Empirical Bayes to approximate posteriors for large "black box" estimators

Webinars

Stay Connected