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Initial Centroids for k-Means

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 (https://orangedatamining.com). SUBSCRIBE to our channel:    / orangedatamining   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 (http://biolab.si/blaz) 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 (https://www.biolab.si)