Difference between revisions of "K-Modes"

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m (BPeat moved page K-modes Clustering to K-Modes Clustering without leaving a redirect)
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[http://www.youtube.com/results?search_query="K+Modes" YouTube search...]
 
[http://www.youtube.com/results?search_query="K+Modes" YouTube search...]
[http://www.youtube.com/results?search_query=k-modes+clustering ...Google search]
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[http://www.google.com/search?q="k-modes"+clustering ...Google search]
  
 
* [[AI Solver]]
 
* [[AI Solver]]
 
** [[...cluster]]
 
** [[...cluster]]
 
* [[Capabilities]]  
 
* [[Capabilities]]  
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* [[K-Means]]
  
a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. This rule-based approach also generates new rules as it analyzes more data. The ultimate goal, assuming a large enough dataset, is to help a machine mimic the human brain’s feature extraction and abstract association capabilities from new uncategorized data.
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an extension of k-means. Instead of distances it uses dissimilarities (that is, quantification of the total mismatches between two objects: the smaller this number, the more similar the two objects). And instead of means, it uses modes. A mode is a vector of elements that minimizes the dissimilarities between the vector itself and each object of the data. We will have as many modes as the number of clusters we required, since they act as centroids. [http://amva4newphysics.wordpress.com/2016/10/26/into-the-world-of-clustering-algorithms-k-means-k-modes-and-k-prototypes/ Into the world of clustering algorithms: k-means, k-modes and k-prototypes | Alessia Saggio]
  
http://annalyzin.files.wordpress.com/2016/04/association-rules-network-graph2.png
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http://i.stack.imgur.com/JqGsg.png
  
  

Revision as of 09:09, 8 January 2019

"K+Modes" YouTube search... "k-modes"+clustering ...Google search

an extension of k-means. Instead of distances it uses dissimilarities (that is, quantification of the total mismatches between two objects: the smaller this number, the more similar the two objects). And instead of means, it uses modes. A mode is a vector of elements that minimizes the dissimilarities between the vector itself and each object of the data. We will have as many modes as the number of clusters we required, since they act as centroids. Into the world of clustering algorithms: k-means, k-modes and k-prototypes | Alessia Saggio


JqGsg.png


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