Classification divisive hierarchical

Guenoche A, Hansen P, Jaumard B, Efficient algorithms for divisive hierarchical clustering with diameter criterion, J. Classif, 8 5-30, 1991. 

The design of very large classifiers. In this case not all the features are relevant for all the objects. In case of a divisive hierarchical classifer, each node of the classifier will contain a set of objects as well as the characteristics that differentiate these objects. 

The fuzzy cross-classification algorithm produces both a fuzzy partition and a fuzzy partition of characteristics compatible with the former. The advantages of this algorithm include the ability to observe not only the fuzzy classes obtained and their relationship, but also the characteristics corresponding to each final class of objects. Each object class may be well described using the corresponding characteristics. These are the characteristics that have contributed to the separation of the respective fuzzy class. Fuzzy divisive hierarchical cross-classification of therapeutic muds based on their physicochemical characteristics allowed an objective interpretation of their origin and maturation and helped in their classification. It also permitted quantitative and qualitative identification of the compo-... 

A. Guenoche, P. Hansen, and B. Jaumard, /. Classif., 8, 5 (1991). Efficient Algorithms for Divisive Hierarchical Clustering widi the Diameter Criterion. 

In order to develop the classifications presented in this section, we will apply the fuzzy divisive hierarchical clustering (FDHiC) procedure described in the theoretical section to different characteristic sets considered here. The hierarchical procedure obtained in this way is called fuzzy hierarchical characteristics clustering (FHiChC). 

Little has been reported on the use of hierarchical divisive methods for processing chemical data sets (other than the inclusion of the minimum-diameter method in some of the comparative studies mentioned above). Recursive partitioning, which is a supervised classification technique very closely related to monothetic divisive clustering, has, however, been used at the GlaxoSmithKline and Organon companies. 

Falconiformes, and Pelecaniformes (for details, see Proctor Lynch, 1993 Sibley Alquist, 1990). In morphological classification systems, the divisions into hierarchies comprising phyla, classes, orders and families are somewhat arbitrary. Using theDNAiDNA hybridisation data, Sibley Alquist (1990) define hierarchical divisions more precisely in terms of the differences in melting temperatures, e.g. classes differ by 31-33 °, orders by 20-22 ° and families by 9-11 °, thus making the hierarchies less arbitrary. 

From this first division, a more detailed classification can be done. The most frequently used ones are SCOP, Structural Classification Of Proteins (Murzin et al., 1995), and CATH, Qass Architecture Topology Homology (Orengo et al., 1997). They are hierarchical classifications... 

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