Artikel,
K-tree: a height balanced tree structured vector quantizer
Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, (2000)
DOI: 10.1109/NNSP.2000.889418
Zusammenfassung
We describe a clustering algorithm for the design of height balanced trees for vector quantisation. The algorithm is a hybrid of the B-tree and the k-means clustering procedure. K-tree supports on-line dynamic tree construction. The properties of the resulting search tree and clustering codebook are comparable to that of codebooks obtained by TSVQ, the commonly used recursive k-means algorithm for constructing vector quantization search trees. The K-tree algorithm scales up to larger data sets than TSVQ, produces codebooks with somewhat higher distortion rates, but facilitates greater control over the properties of the resulting codebooks. We demonstrate the properties and performance of K-tree and compare it with TSVQ and with k-means
