--kmeans Algorithm of Clustering algorithm

Source: Internet
Author: User

Clustering concepts

Unsupervised problem: We don't have a label in hand.

Clustering: Something similar is divided into a group

Difficulties: How to evaluate, how to adjust the parameters


Basic concepts

To get the number of clusters, you need to specify a K value

Centroid: Mean, that is, the average of vector dimensions can be

Measure of distance: Euclidean distance and cosine (normalized first)

Optimization objectives:


Work Flow:



Simple, fast, and suitable for general data sets


K value difficult to determine

The complexity is linearly related to the sample

It is difficult to find clusters of arbitrary shapes


--kmeans Algorithm of Clustering algorithm

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