Description: Hard and soft k-means implemented simply in python (with numpy). Quick and dirty, tested and works on large (10k+ observations, 2-10 features) real-world data.
Related: data mining, machine learning
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The k-means algorithm is widely used in a number applications like speech processing and image compression.
This script implements the algorithm in a simple but general way. It performs four basic steps.
1. Define k arbitrary prototypes from the data samples.
2. Assign each sample to the nearest prototype.
3. Recalculate prototypes as arithmetic means.
4. If a prototype changes, repeat step (2).
Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.
An implementation of "k-Means Projective Clustering" by P. K. Agarwal and N. H. Mustafa.
This method of clustering is based on finding few subspaces such that each point is close to a subspace.
K-means image segmentation based on histogram to reduce memory usage which is constant for any image size.
This is a tool for K-means clustering. After trying several different ways to program, I got the conclusion that using simple loops to perform distance calculation and comparison is most efficient and accurate because of the JIT acceleration in...
[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X
into k (or, rarely, fewer) clusters by applying the well known batch
K-means algorithm. Rows of X correspond to points, columns correspond to
KNMCluster is an implementation of the K-means clustering algorithm. It takes inputs k and indata. k is the initial guess of the number of clusters.
indata is the aggregate data that you...
Description DC is simple and effective which can outperform the K-means and AP algorithm.
PBKM is simple and effective which can outperform the K-means algorithm.
This is an implementation of the paper
k-means++: the advantages of careful seeding.
It converges very quickly.
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