The Classification Society Bulletin

This book provides a coherent account of the Theory of Classification. It discusses the contribution made by theoreticians like E.C. Richardson, J.D. Brown, W. Hulum, W.C. Berwick Sayers, H.E. Bliss and S.R. Ranganathan. However, the theory put forward by S.R. Ranganathan predominates the whole book because his contribution is far more than anybody else’s. Five major schemes of Classification, Library of Congress Classification, Colon Classification and Bliss Biblio-Graphic Classification have also been discussed.

"Legend is overdue for replacement, and an adequate replacement must attend to the process of science as carefully as Hull has done. I share his vision of a serious account of the social and intellectual dynamics of science that will avoid both the rosy blur of Legend and the facile charms of relativism. . . . Because of [Hull's] deep concern with the ways in which research is actually done, Science as a Process begins an important project in the study of science. It is one of a distinguished series of books, which Hull himself edits."—Philip Kitcher, Nature "In Science as a Process, [David Hull] argues that the tension between cooperation and competition is exactly what makes science so successful. . . . Hull takes an unusual approach to his subject. He applies the rules of evolution in nature to the evolution of science, arguing that the same kinds of forces responsible for shaping the rise and demise of species also act on the development of scientific ideas."—Natalie Angier, New York Times Book Review "By far the most professional and thorough case in favour of an evolutionary philosophy of science ever to have been made. It contains excellent short histories of evolutionary biology and of systematics (the science of classifying living things); an important and original account of modern systematic controversy; a counter-attack against the philosophical critics of evolutionary philosophy; social-psychological evidence, collected by Hull himself, to show that science does have the character demanded by his philosophy; and a philosophical analysis of evolution which is general enough to apply to both biological and historical change."—Mark Ridley, Times Literary Supplement "Hull is primarily interested in how social interactions within the scientific community can help or hinder the process by which new theories and techniques get accepted. . . . The claim that science is a process for selecting out the best new ideas is not a new one, but Hull tells us exactly how scientists go about it, and he is prepared to accept that at least to some extent, the social activities of the scientists promoting a new idea can affect its chances of being accepted."—Peter J. Bowler, Archives of Natural History "I have been doing philosophy of science now for twenty-five years, and whilst I would never have claimed that I knew everything, I felt that I had a really good handle on the nature of science, Again and again, Hull was able to show me just how incomplete my understanding was. . . . Moreover, [Science as a Process] is one of the most compulsively readable books that I have ever encountered."—Michael Ruse, Biology and Philosophy

The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.