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.