We propose a novel class of learning vector quantizers (LVQs) based on multivariate data ordering principles. A special case of the novel LVQ class is the median LVQ, which uses either the marginal median or the vector median as a multivariate estimator of location. The performance of the proposed marginal median LVQ in color image quantization is demonstrated by experiments.

Data mining is an important tool in meteorological problems solved. Cluster analysis techniques in data mining play an important role in the study of meteorological applications. The research progress of the clustering algorithms in meteorology in recent years is summarized in this paper. First, we give a brief introduction of the principles and characteristics of the clustering algorithms that are commonly used in meteorology. On the other hand, the applications of clustering algorithms in meteorology are analyzed, and the relationship between the various clustering algorithms and meteorological applications are summarized. Then we interpret the relationship from the perspectives of algorithms' characteristics and practical applications. Finally, some main research issues and directions of the clustering algorithms in meteorological applications are pointed out.

This research progress of the clustering algorithms in meteorology in recent years is summarized in this paper. First, we give a brief introduction of the principles and characteristics of the clustering algorithms that are commonly used in meteorology. On the other hand, the applications of clustering algorithms in meteorology are analyzed, and the relationship between the various clustering algorithms and meteorological applications are summarized. Then we interpret the relationship from the perspectives of algorithms' characteristics and practical applications. Finally, some main research issues and directions of the clustering algorithms in meteorological applications are pointed out.