Showing papers in "Advances in Computers in 1980"

Clustering Methodologies in Exploratory Data Analysis

Abstract: Publisher Summary This chapter reviews cluster analysis and related topics or the formal study of classification schemata, whereby objects are grouped, or clustered, according to measured or perceived intrinsic characteristics. The objective of a cluster analysis is to uncover natural groupings, or types, to prod one's creativity and ingenuity, and initiate hypotheses about the phenomenon being studied. Cluster analysis has a heuristic nature that encourages the exploration of data. Taxonomists, social scientists, psychologists, biologists, statisticians, mathematicians, engineers, computer scientists, medical researchers, and others who handle real data have all contributed to clustering methodology. The chapter presents cross-disciplinary communication so that one application area can profit from the experiences of others. The literature of cluster analysis straddles all quantitative, scientific disciplines, as demonstrated by the remarkable variety. Emphasis is on new developments, especially in the verification and validation of clustering results. The intent is to provide an applications-oriented treatment of cluster analysis in the spirit of exploratory data analysis. The chapter focuses on the four operations highlighted by reviewing techniques for assessing the tendency of the data to cluster, performing the clustering itself, and evaluating the validity of the results. Data representation includes recognition of data type and scale, measures of proximity and affinity, normalization, various two-dimensional projections of data, methods of visualizing multidimensional data, techniques for creating scales to describe data, and related matters. The chapter introduces the concept of intrinsic dimensionality that helps determine an appropriate number of factors for representing data. The chapter recommends that a serious data analyst be conversant with as broad a range of data analysis techniques and programs as possible and be aware of the assumptions on which the techniques are based.

The Structure of Parallel Algorithms.

Abstract: Publisher Summary This chapter presents many examples of parallel algorithms and studies them under a uniform framework. The chapter explains a parallel algorithm as a collection of independent task modules that can be executed in parallel and that communicate with each other during the execution of the algorithm. The chapter explains the three important attributes of a parallel algorithm and classifies parallel algorithms in terms of these attributes. Three orthogonal dimensions of the space of parallel algorithms: concurrency control, module granularity, and communication geometry. The classification of parallel algorithms corresponds naturally to that of parallel architectures. Algorithms for synchronous parallel computers are considered, where examples of algorithms using various communication geometries are presented. Algorithms for asynchronous parallel computers are also considered in the chapter. A number of techniques dealing with the difficulties arising from the asynchronous behavior of computation and the examples are mainly drawn from results in concurrent database systems. This chapter deals with the basic issues and techniques in designing parallel algorithms for various architectures. The chapter concludes that issues concerning algorithms for synchronous parallel computers are quite different from those for asynchronous parallel computers.

Computing as Social Action: The Social Dynamics of Computing in Complex Organizations

Abstract: Publisher Summary This chapter examines the ways in that the behavior of people and groups in organizations influences the development, use, and consequences of computing. The chapter presents an examination of the usefulness of different social perspectives for explaining how computing developments work in complex organizations. The chapter also presents that the six perspectives are best introduced by indicating how they help explain a complex case of computer use. The six theoretical perspectives help to understand the assumptions behind the questions asked and the answers different analysts have found. The rational perspective dominates the majority of analyses of computing, particularly those that are written by practitioners and found in trade journals or the internal documents of organizations. The development, use, and impact of computing in organizations are examined in light of the six perspectives outlined. The chapter examines the development and provision of computer services through the life cycle from initiation to evaluation. The knowledge about computing is distributed throughout organizations, and this leads to systematic misperceptions of computer use and increases the likelihood of computing errors. The chapter examines the consequences of computer use for the ways decisions are made, the work lives of computer users, and the distributions of power in computer-using organizations.

Numerical Software: Science or Alchemy?

Abstract: Publisher Summary Numerical software is a term that is used rather liberally today to describe a range of activities. It has two principle characteristics: it deals with approximations to real numbers and it is usable on a range of computers that have different approximation capabilities. The nature of numerical software is examined and the chapter then discusses what is particularly difficult about it. Numerical software production is viewed by many people either as a routine programming task or as a by-product of that dull subject, numerical analysis that itself falls somewhere between mathematics and computer science, too applied for the one and too irrelevant to the other. The chapter explains that some of the attributes needed in numerical software are reliability, efficiency, and broad applicability. Numerical software is very much a part of computer science. It is concerned with automatic problem solving—that is, with analyzing methods and synthesizing techniques. It has some solid scientific foundations, but still involves a great deal of judgment because the idealized goals are not achievable. The chapter concludes that the numerician is partially reduced to alchemy; for brewing new concoctions and testing the results, sometimes heuristics are used.