Continuing with k-means, we take a look at initial placement of centroids. Does it make a difference? How about how many of them there are in the beggining? Can k-Means get stuck on a solution? In this video, we leave the Interactive k-Means widget behind and introduce KMeans++ alongside our regular k-Means widget.
This video is a part of Introduction to Data Science video series that dives into machine learning, visual analytics, and joys of interactive data analysis using Orange Data Mining software ([ Ссылка ]).

SUBSCRIBE to our channel: [ Ссылка ]

The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation.

#machinelearning #orange #visualanalytics #datamining
__

Written by: Blaž Zupan ([ Ссылка ])
Presented by: Noah Novšak
Production and edit: Lara Zupan
Intro/outro: Agnieszka Rovšnik
Music by: Damjan Jović – Dravlje Rec
Orange is developed by Biolab at University of Ljubljana ([ Ссылка ])

свернуть