PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Visualization support for the analysis of properties of interestingness measures

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper considers a particular group of rule interestingness measures, called Bayesian confirmation measures, which have become the subject of numerous, but often exclusively theoretical studies. To assist and enhance their analysis in real-life situations, where time constraints may impede conducting such time consuming procedures, a visual technique has been introduced and described in this paper. It starts with an exhaustive and non-redundant set of contingency tables, which consists of all possible tables having the same number of observations. These data, originally 4-dimensional, may, owing to an inherent constraint, be effectively represented as a 3-dimensional tetrahedron, while an additional, scalar function of the data (e.g. a confirmation measure) may be rendered using colour. Dedicated analyses of particular colour patterns on this tetrahedron allow to promptly perceive particular properties of the visualized measures. To illustrate the introduced technique, a set of 12 popular confirmation measures has been selected and visualized. Additionally, a set of 9 popular properties has been chosen and the visual interpretations of the measures in terms of the properties have been presented.
Rocznik
Strony
315--327
Opis fizyczny
Bibliogr. 26, wykr., rys., tab.
Twórcy
autor
  • Institute of Computing Science, Poznań Univesity of Technology, 2 Piotrowo St., 60-965 Poznań, Poland
autor
  • Institute of Computing Science, Poznań Univesity of Technology, 2 Piotrowo St., 60-965 Poznań, Poland
Bibliografia
  • [1] U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, Berlin, 2002.
  • [2] K. Napierała and J. Stefanowski, “Bracid: a comprehensive approach to learning rules from imbalanced data”, J. Intelligent Information Systems 39 (2), 335-373 (2012).
  • [3] I. Brzezińska, S. Greco, and R. Słowiński, “Mining paretooptimal rules with respect to support and anti-support”, Engineering Applications of Artificial Intelligence 20 (5), 587-600 (2007).
  • [4] S. Greco, Z. Pawlak, and R. Słowiński, “Can bayesian confirmation measures be useful for rough set decision rules?”, Engineering Applications of Artifficial Intelligence 17, 345-361 (2004).
  • [5] I. Szcze,ch, “Multicriteria attractiveness evaluation of decision and association rules”, Transactions on Rough Sets X, LNCS Series 5656, 197-274 (2009).
  • [6] V. Crupi, K. Tentori, and M. Gonzalez, “On bayesian measures of evidential support: Theoretical and empirical issues”, Philosophy of Science 74, 229-252 (2007).
  • [7] S. Greco, R. Słowiński, and I. Szcze,ch, “Properties of rule interestingness measures and alternative approaches to normalization of measures”, Information Sciences 216, 1-16 (2012).
  • [8] B. Fitelson, “Studies in Bayesian confirmation theory”, PhD Thesis, University of Wisconsin, Madison, 2001.
  • [9] E. Eells and B. Fitelson, “Symmetries and asymmetries in evidential support”, Philosophical Studies 107 (2), 129-142 (2002).
  • [10] S. Greco, R. Słowiński, and I. Szcze,ch, “Analysis of symmetry properties for bayesian confirmation measures”, Rough Sets and Knowledge Technology - 7th Int. Conf. (RSKT 2012), 207-214 (2012).
  • [11] S. Greco, R. Słowiński, and I. Szcze,ch, “Finding meaningful bayesian confirmation measures”, Fundamenta Informaticae 127 (1-4), 161-176 (2013).
  • [12] D.H. Glass, “Confirmation measures of association rule interestingness”, Knowlegde Based Systems 44, 65-77 (2013).
  • [13] R. Susmaga and I. Szcze,ch, “Can interestingness measures be usefully visualized?”, Int. J. Applied Mathematics and Computer Science (2015), (to be published).
  • [14] R. Susmaga and I. Szcze,ch, “Visualization of interestingness measures”, Proc. 6th Language & Technology Conf.: Human Language Technologies as a Challenge for Computer Science and Linguistics 1, 95-99 (2013).
  • [15] R. Susmaga and I. Szcze,ch, “Visual-based detection of properties of confirmation measures”, Lecture Notes in Computer Science, ISMIS 8502, 133-143 (2014).
  • [16] L. Geng and H. Hamilton, “Interestingness measures for data mining: A survey”, ACM Computing Surveys 38, 3 (2006).
  • [17] S. Greco, R. Słowiński, and I. Szcze,ch, “Property of confirmation”, in Tech. Rep. RA-01/14, Poznań University of Technology, Poznań, 2014.
  • [18] B. Fitelson, “The plurality of bayesian measures of confirmation and the problem of measure sensitivity”, Philosophy of Science 66, 362-378 (1999).
  • [19] E. Eells, Rational Decision and Causality, Cambridge University Press, Cambridge, 1982.
  • [20] H. Mortimer, The Logic of Induction, Paramus, Prentice Hall, 1988.
  • [21] D. Christensen, “Measuring confirmation”, J. Philosophy 96, 437-461 (1999).
  • [22] R. Nozick, Philosophical Explanations, Clarendon Press, Oxford, 1981.
  • [23] R. Carnap, Logical Foundations of Probability, University of Chicago, Chicago, 1962.
  • [24] J. Kemeny and P. Oppenheim, “Degrees of factual support”, Philosophy of Science 19, 307-324 (1952).
  • [25] R. Susmaga and I. Szczęch, “Statistical significance of bayesian confirmation measures”, in Tech. Rep., RA-010/12, Poznań University of Technology, Poznań, 2012.
  • [26] P. Lenca, P. Meyer, B. Vaillant, and S. Lallich, “On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid”, Eur. J. Operational Research 184 (2), 610-626 (2008).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7705357d-285e-48b4-b079-86af3b3fbb78
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.