Algorithms for clustering data
Algorithms for clustering data
Applied multivariate statistical analysis
Applied multivariate statistical analysis
Optimal quantization by matrix searching
Journal of Algorithms
Color quantization by dynamic programming and principal analysis
ACM Transactions on Graphics (TOG)
Finding a minimum weight K-link path in graphs with Monge property and applications
SCG '93 Proceedings of the ninth annual symposium on Computational geometry
Self-organization as an iterative kernel smoothing process
Neural Computation
ACM Computing Surveys (CSUR)
Learning and Design of Principal Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm with deterministic crossover for vector quantization
Pattern Recognition Letters
Self-Organizing Maps
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A k-segments algorithm for finding principal curves
Pattern Recognition Letters
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Principal curves with bounded turn
IEEE Transactions on Information Theory
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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.