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k-means clustering

Given the set of n data points x_i, i =1,n (m-dimensional vectors (x_i,1, ... , x_i,m) ), k - means clustering partitions them into set
C = {C₁, ..., C_k} of k clusters, so that each data point x_i belongs to the j cluster C_j with the nearest mean μ_j (cluster centers or cluster centroids):
C_j = argmin _C

k
∑
j=1


∑		‖ x_i - μ_j ‖²
x_i ∈ C_j

, where μ_j = 1| C_j |


∑		x
x ∈ C_j

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Please Note:

We have limited the dimension m in the "Manual Entry" tab to 10 due to the limited space on the screen.
Rows (data points) which have all of the cells empty will be disregarded in calculations.

Number of Clusters:Dimension:

#x₁x₂

1
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10

See Also:	Fuzzy C-Means Clustering of points in n-dimensional space

External links:

Wikipedia: k-means clustering

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Clusters: