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Fundamentals of Data Mining

Data Science 101

This data alone does not make any sense unless it’s identified to be related in some pattern. Data mining is the process of discovering these patterns among the data and is therefore also known as Knowledge Discovery from Data (KDD). You might be wondering what benefit you can get out of these techniques?

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. In this blog post, we summarize that paper and refer you to it for details. 2] Ron Kohavi, Randal M.

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Density-Based Clustering

Domino Data Lab

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).

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

The Unofficial Google Data Science Blog

For more on ad CTR estimation, refer to [2]. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2] A machine learning system produces an estimated CTR $t_i$ for each query-ad pair. Our method has four steps: Bin by $t$.

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