DBSCAN is a powerful clustering algorithm used in various machine learning applications. A wide range of research has focused on clustering geographical Points Of Interest (POI), in an unsupervised way. Most of the existing approaches use DBSCAN coordinates as input but do not include other features in the clustering process.

In this article, we introduce a multi-feature model to geo clustering with DBSCAN allowing for more features than the sole location. We present an example of an application suggesting that this approach can in some cases perform better.

How DBSCAN works

How DBSCAN works — from Wikipedia

DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is…

Neural networks explained to a majority of Computer Science students by a Mechanical Engineering student during a DataVenture event — Photo by Bastien Le Moallic

Data Science is a trendy new field taught in most universities — at least scientific and engineering ones. But still in 2018, it’s not always seen and understood as a complete and strong problem solving approach in every major. I believe it should be!

Data Science covers so many different things: data management, statistics, data visualisation, machine learning and so on. People state that only computer scientists are skilled enough to deal with this. Why? Because it often requires programming skills which are not yet taught to everyone at high school (nor before) as basic knowledge. …

Opéra Garnier, Paris, near former craft ai offices — Photo by Sebastien Gabriel

I spent 7 months as an intern at craft ai, a machine learning API focused on learning habits. My main project was about making predictions on arrival time of waste collection trucks in the streets of Paris. If you’re interested, I wrote a blog post explaining how the overall solution works.

Retrospectively, I want to sum up the 9 main elements I’ve learned. Firstly, 3 technicals lessons about machine learning realities to keep in mind. Secondly, the 3 main skills every data scientist should have. …

“Data Science”, c’est un terme de plus en plus populaire et qui tend à devenir un maître mot pour désigner beaucoup de domaines scientifiques et du domaine de l’ingénieur : Big Data, Intelligence Artificielle, Graphes et Réseaux, Data mining, Statistiques… Mais concrètement, pourquoi serait-ce nécessaire de s’y intéresser et quel avenir nous réservent les données ?

90%, c’est la part totale des données actuelles mondiales qui ont été générées sur les deux seules dernières années parmi l’ensemble de toutes les données générées.

47%, c’est le pourcentage d’emplois actuels qui devraient être totalement remplacés sur les 20 prochaines années.

300%, c’est…

Sylvain Marchienne

Machine Learning Engineer

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store