From Sets of Good Redescriptions to Good Sets of Redescriptions

International audienceRedescription mining aims at finding pairs of queries over data variables that describe roughly the same set of observations. These redescriptions can be used to obtain different views on the same set of entities. So far, redescription mining methods have aimed at listing all redescriptions supported by the data. Such an approach can result in many redundant redescriptions and hinder the user's ability to understand the overall characteristics of the data. In this work, we present an approach to find a good set of redescriptions, instead of finding a set of good redescriptions. That is, we present a way to remove the redundant redescriptions from a given set of redescriptions. We measure the redundancy using a framework inspired by the subjective interestingness based on maximum-entropy distributions as proposed by De Bie in 2011. Redescriptions, however, raise their unique requirements on the framework, and our solution differs significantly from the existing ones. Notably, our approach can handle disjunctions and conjunctions in the queries, whereas the existing approaches are limited only to conjunctive queries. The framework also reduces the redundancy in the redescription mining results, as we show in our empirical evaluation

Uncertainty behind the veil of ignorance

This paper argues that the decision problem in the original position should be characterized as a decision problem under uncertainty even when it is assumed that the denizens of the original position know that they have an equal chance of ending up in any given individual's place. It argues for this claim by arguing that (a) the continuity axiom of decision theory does not hold between all of the outcomes the denizens of the original position face and that (b) neither us nor the denizens of the original position can know the exact point where discontinuity sets in, because the language we employ in comparing different outcomes is ineradicably vague. It is also argued that the account underlying (b) can help proponents of superiority in value theory defend their view against arguments offered by Norcross and Griffin

Contingency, Irony and Morality: A Critical Review of Rorty's. Notion of the Liberal Utopia

This paper introduces Richard Rorty’s notion of the liberal ironist and his vision of a liberal utopia and explores the implications of these for philosophical questions concerning morality, as well as morality in general. Rorty’s assertions of the contingency of language, society and self are explored. Under the contingency of language, the figure of the ironist is defined, and Rorty’s conception of vocabularies is discussed. Under the contingency of society, Rorty’s definition of liberalism, his opposition of literary culture to materialist and metaphysical culture, and his notions concerning utopian politics are discussed. Under the contingency of self, Rorty’s critique of Kantian and his appropriations of Deweyan and Freudian conceptions of morality are presented. Other key factors discussed are Rorty’s theory of the separation of the private and public spheres of life and his ideas concerning cruelty and human solidarity. In this way, a critical analysis of Rorty’s proposed balance between private, ironic doubt and public, liberal social hope is presented and assessed in terms of its merit as a system of thought suited to the needs of post-metaphysical, liberal societies

Finding relational redescriptions

We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline Inductive Logic Programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks

Redescription Mining: An Overview.

International audienceIn many real-world data analysis tasks, we have different types of data over the same objects or entities, perhaps because the data originate from distinct sources or are based on different terminologies. In order to understand such data, an intuitive approach is to identify thecorrespondences that exist between these different aspects. This isthe motivating principle behind redescription mining, a data analysistask that aims at finding distinct commoncharacterizations of the same objects.This paper provides a short overview of redescription mining; what it is and how it is connected to other data analysis methods; the basic principles behind current algorithms for redescription mining; and examples and applications of redescription mining for real-world data analysis problems