title: A graphical notation for the design of information visualisations

authors: Humphrey, Matthew C.

subjects: computer science

abstract: Visualisations are coherent, graphical expressions of complex information that enhance people’s ability to communicate and reason about that information. Yet despite the importance of visualisations in helping people to understand and solve a wide variety of problems, there is a dearth of formal tools and methods for discussing, describing and designing them. Although simple visualisations, such as bar charts and scatterplots, are easily produced by modern interactive software, novel visualisations of multivariate, multirelational data must be expressed in a programming language. The Relational Visualisation Notation is a new, graphical language for designing such highly expressive visualisations that does not use programming constructs. Instead, the notation is based on relational algebra, which is widely used in database query languages, and it is supported by a suite of direct manipulation tools. This article presents the notation and examines the designs of some interesting visualisations.

text: A graphical notation for the design of information visualisations Visualisations are coherent, graphical expressions of complex information that enhance people’s ability to communicate and reason about that information. Yet despite the importance of visualisations in helping people to understand and solve a wide variety of problems, there is a dearth of formal tools and methods for discussing, describing and designing them. Although simple visualisations, such as bar charts and scatterplots, are easily produced by modern interactive software, novel visualisations of multivariate, multirelational data must be expressed in a programming language. The Relational Visualisation Notation is a new, graphical language for designing such highly expressive visualisations that does not use programming constructs. Instead, the notation is based on relational algebra, which is widely used in database query languages, and it is supported by a suite of direct manipulation tools. This article presents the notation and examines the designs of some interesting visualisations.

year: 1997-02

title: One-Class Classification by Combining Density and Class Probability Estimation

authors: Hempstalk, Kathryn

subjects: computer science

abstract: One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.

text: One-Class Classification by Combining Density and Class Probability Estimation One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.

year: 2008

title: Māori & Psychology Research Unit Annual Report 2015

authors: Waitoki, Waikaremoana

abstract: The Māori and Psychology Research Unit (MPRU) was established in August of 1997. The Unit acts as a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Māori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the School of Psychology, the Faculty of Arts and Social Sciences, the wider University and general community.

text: Māori & Psychology Research Unit Annual Report 2015 The Māori and Psychology Research Unit (MPRU) was established in August of 1997. The Unit acts as a catalyst and support network for enhancing research concerning the psychological needs, aspirations, and priorities of Māori people. The MPRU is well situated to draw together skilled and experienced interdisciplinary research groups by networking and establishing working relationships with staff and students within the School of Psychology, the Faculty of Arts and Social Sciences, the wider University and general community.

year: 2015