Generalization of the codebook updating method of the k-means algorithm

At each iteration of codebook training, the K-means algorithm finds a new codevector and partition by satisfying the centroid condition and the nearest neighbor condition alternatively and satisfies the iterative descent property. We generalize the codebook updating method of the K-means algorithm w...

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Bibliographic Details
Main Authors: Daeryong Lee, Jinsoo Jung, Koengmo Sung
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Design methodology
Distortion measurement
Iterative algorithms
Iterative methods
Multiaccess communication
Nearest neighbor searches
Partitioning algorithms
Training data
Vector quantization
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Summary:At each iteration of codebook training, the K-means algorithm finds a new codevector and partition by satisfying the centroid condition and the nearest neighbor condition alternatively and satisfies the iterative descent property. We generalize the codebook updating method of the K-means algorithm without affecting the iterative descent property. We also present and re-interpret several algorithms that are included into the category of the generalized K-means algorithm.
DOI:10.1109/APCC.1999.824946