||Cleju, I., Fränti, P., Wu, X.
||Clustering based on principal curve
||Clustering algorithms are intensively used in the image
analysis field in compression, segmentation, recognition and other
tasks. In this work we present a new approach in clustering vector
datasets by finding a good order in the set, and then applying an
optimal segmentation algorithm. The algorithm heuristically prolongs the
optimal scalar quantization technique to vector space. The data set is
sequenced using one-dimensional projection spaces. We show that the
principal axis is too rigid to preserve the adjacency of the points. We
present a way to refine the order using the minimum weight Hamiltonian
path in the data graph. Next we propose to use the principal curve to
better model the non-linearity of the data and find a good sequence in
the data. The experimental results show that the principal curve based
clustering method can be successfully used in cluster analysis.