Arndt van Haeseler – författare
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2 produkter
Häftad, Engelska, 1990
544 kr
Skickas inom 10-15 vardagar
The non-transitivity of similarity relations is the main problem encountered when - as in taxonomic studies in biology - one wants to base classification schemes on observed similarities and dissimilarities. While recent advances in molecular biology give rise to new and rather abstract data structures which can easily be used as input for automatic classification procedures we are still very much in need of a better and deeper understanding of the many delicate points which need consideration once (semi-)automatic classification procedures are applied to biological or other data. The papers collected in this volume are devoted to precisely this problem. They study various theoretical aspects of three reconstruction methods in biology, and psychology, discuss their value in specific biological contexts, apply tree-like recursion networks in chess programming and indicate a conceptual framework for studying cluster analysis from a purely mathematical point of view.
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The "raison d''etre" of hierarchical dustering theory stems from one basic phe nomenon: This is the notorious non-transitivity of similarity relations. In spite of the fact that very often two objects may be quite similar to a third without being that similar to each other, one still wants to dassify objects according to their similarity. This should be achieved by grouping them into a hierarchy of non-overlapping dusters such that any two objects in ~ne duster appear to be more related to each other than they are to objects outside this duster. In everyday life, as well as in essentially every field of scientific investigation, there is an urge to reduce complexity by recognizing and establishing reasonable das sification schemes. Unfortunately, this is counterbalanced by the experience of seemingly unavoidable deadlocks caused by the existence of sequences of objects, each comparatively similar to the next, but the last rather different from the first.