BIRCH : Différence entre versions


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==Français==
 
==Français==
 
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<math> \Delta^{(k)} \, w_{ij} = \Delta^{(k-1)} \, w_{ij} \left ( \frac{\nabla_{ij} \, E^{(k)}}{\nabla_{ij} \, E^{(k-1)} - \nabla_{ij} \, E^{(k)}} \right) </math>
 
  
 
==Anglais==
 
==Anglais==

Version du 20 décembre 2020 à 11:40

en construction

Définition

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Français

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Anglais

BIRCH

BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets.[1] An advantage of BIRCH is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, BIRCH only requires a single scan of the database.

Its inventors claim BIRCH to be the "first clustering algorithm proposed in the database area to handle 'noise' (data points that are not part of the underlying pattern) effectively",[1] beating DBSCAN by two months. The algorithm received the SIGMOD 10 year test of time award in 2006.[2]

: Wikipedia Machine Learning

Récupérée de « https://datafranca.org/wiki/BIRCH »