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Corrected capitalization, corrected to American spellings and typos (#30685)

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* Translate challenge subtitles and example challenge text to Spanish

* Corrected errors in syntax and punctuation

* Multiple corrections of it/s to its plus other grammar corrections

* Correction and added paragraph to CSS Flex article

* Corrected my own typo

* Corrected capitalization, American spellings and typos
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S.Hale
2018-11-07 15:34:13 +00:00
committed by Paul Gamble
parent 624f328f29
commit 3920b7c4ce
119 changed files with 551 additions and 536 deletions
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guide/english/machine-learning/clustering-algorithms/index.md
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@ -35,7 +35,7 @@ There are methods for clustering that only use similarities of instances, withou
This needs the use of a similarity, or equivalently a distance, measure defined between instances. Generally Euclidean distance is used, where one has to make sure that all attributes have the same scale.
There are two main types of Hierarchical clustering which are used:
1. Agglomerative Clustering - This algorithm starts with a bunch of individual clusters and a proximity matrix. Here, the individual clusters are basically individual points, and the matrix is for the distance between each point with each other points. The algorithm tries to find the closest pair of clusters and then combines them into one cluster, and then update the proximity matrix with the new cluster and removes the two combined clusters. This step is repeated until a single cluster is left. The most important part of this algorithm is the proximity matrix and it's updatation.
1. Agglomerative Clustering - This algorithm starts with a bunch of individual clusters and a proximity matrix. Here, the individual clusters are basically individual points, and the matrix is for the distance between each point with each other points. The algorithm tries to find the closest pair of clusters and then combines them into one cluster, and then update the proximity matrix with the new cluster and removes the two combined clusters. This step is repeated until a single cluster is left. The most important part of this algorithm is the proximity matrix and its updatation.
2. Divisive Clustering - This algorithm can be called an opposite of Agglomerative in terms of how it approachs clustering. It starts with a single cluster and then starts dividing it into multiple clusters. It has a similarity matrix between each point, similarity here being how close the clusters are with each other. This algorithm tries to divide the cluster into two clusters based on how dissimilar a cluster or a point is from the rest. This is continued until there are multiple individual clusters.
### Point Assignment