PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Can interestingness measures be usefully visualized?

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents visualization techniques for interestingness measures. The process of measure visualization provides useful insights into different domain areas of the visualized measures and thus effectively assists their comprehension and selection for different knowledge discovery tasks. Assuming a common domain form of the visualized measures, a set of contingency tables, which consists of all possible tables having the same total number of observations, is constructed. These originally four-dimensional data may be effectively represented in three dimensions using a tetrahedron-based barycentric coordinate system. At the same time, an additional, scalar function of the data (referred to as the operational function, e.g., any interestingness measure) may be rendered using colour. Throughout the paper a particular group of interestingness measures, known as confirmation measures, is used to demonstrate the capabilities of the visualization techniques. They cover a wide spectrum of possibilities, ranging from the determination of specific values (extremes, zeros, etc.) of a single measure, to the localization of pre-defined regions of interest, e.g., such domain areas for which two or more measures do not differ at all or differ the most.
Rocznik
Strony
323--336
Opis fizyczny
Bibliogr. 31 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland
autor
  • Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland
Bibliografia
  • [1] Agrawal, R., Imielinski, T. and Swami, A. (1993). Mining associations between sets of items in massive databases, Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data, Washington, DC, USA, pp. 207–216.
  • [2] Alaíz-Rodríguez, R., Japkowicz, N. and Tischer, P.E. (2008). Visualizing classifier performance on different domains, ICTAI 2008, Dayton, OH, USA, pp. 3–10.
  • [3] Carnap, R. (1962). Logical Foundations of Probability, 2nd Edn., University of Chicago Press, Chicago, IL.
  • [4] Christensen, D. (1999). Measuring confirmation, Journal of Philosophy 96(9): 437–461.
  • [5] Crupi, V., Tentori, K. and Gonzalez, M. (2007). On Bayesian measures of evidential support: Theoretical and empirical issues, Philosophy of Science 74(2): 229–252.
  • [6] Drummond, C. and Holte, R.C. (2006). Cost curves: An improved method for visualizing classifier performance, Machine Learning 65(1): 95–130.
  • [7] Eells, E. (1982). Rational Decision and Causality, Cambridge University Press, Cambridge.
  • [8] Everson, R.M. and Fieldsend, J.E. (2006). Multi-class ROC analysis from a multi-objective optimisation perspective, Pattern Recognition Letters 27(8): 918–927.
  • [9] Fitelson, B. (1999). The plurality of Bayesian measures of confirmation and the problem of measure sensitivity, Philosophy of Science 66: 362–378.
  • [10] Fitelson, B. (2001). Studies in Bayesian Confirmation Theory, Ph.D. thesis, University of Wisconsin, Madison, WI.
  • [11] Floater, M.S., Hormann, K. and Kos, G. (2006). A general construction of barycentric coordinates over convex polygons, Advances in Computational Mathematics 24(1–4): 311–331.
  • [12] Fukuda, T., Morimoto, Y., Morishita, S. and Tokuyama, T. (1996). Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization, Proceedings of the 1996 ACM-SIGMOD International Conference on the Management of Data, Montreal, Quebec, Canada, pp. 13–23.
  • [13] Geng, L. and Hamilton, H. (2006). Interestingness measures for data mining: A survey, ACM Computing Surveys 38(3), Article no. 9.
  • [14] Greco, S., Pawlak, Z. and Słowiński, R. (2004). Can Bayesian confirmation measures be useful for rough set decision rules?, Engineering Applications of Artificial Intelligence 17(4): 345–361.
  • [15] Greco, S., Słowiński, R. and Szczęch, I. (2012). Properties of rule interestingness measures and alternative approaches to normalization of measures, Information Sciences 216: 1–16.
  • [16] Healey, C. (1996). Choosing effective colors for data visualization, Proceedings of the 7th Conference on Visualization, VIS’96, San Francisco, CA, USA, pp. 263–270.
  • [17] Hernández-Orallo, J., Flach, P.A. and Ramirez, C.F. (2011). Brier curves: A new cost-based visualisation of classifier performance, Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, WA, USA, pp. 585–592.
  • [18] IBM (1996). Intelligent miner user guide, Version 1, Release 1, Technical report, International Business Machines, San Jose, CA.
  • [19] Kemeny, J. and Oppenheim, P. (1952). Degrees of factual support, Philosophy of Science 19(4): 307–324.
  • [20] Mortimer, H. (1988). The Logic of Induction, Prentice Hall, Paramus, NJ.
  • [21] Morzy, T. and Zakrzewicz, M. (2003). Data mining, in J. Blazewicz,W. Kubiak, T.Morzy and M.E. Rusinkiewicz (Eds.), Handbook on Data Management Information Systems, Springer, Heidelberg, pp. 487–565.
  • [22] Nozick, R. (1981). Philosophical Explanations, Clarendon Press, Oxford.
  • [23] Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes’ theorem, European Journal of Operational Research 136(1): 181–189.
  • [24] Pawlak, Z. (2004). Some issues on rough sets, Transactions on Rough Sets I, Elsevier Science Publishers, New York, NY, pp. 1–58.
  • [25] Shaikh, M., McNicholas, P.D., Antonie, M.L. and Murphy, T.B. (2013). Standardizing interestingness measures for association rules, Computing Research Repository, http://arxiv.org/abs/1308.3740.
  • [26] Susmaga, R. and Szczęch, I. (2013). Visualization of interestingness measures, Proceedings of the 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Poznań, Poland, pp. 95–99.
  • [27] Susmaga, R. and Szczęch, I. (2014). Visual-based detection of properties of confirmation measures, in T. Andreasen, H. Christiansen, J.C.C. Talavera and Z.W. Ras (Eds.), Proceedings of the 21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, Lecture Notes in Computer Science, Vol. 8502, Springer, Heidelberg, pp. 133–143.
  • [28] Tan, P., Kumar, V. and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns, Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 32–41.
  • [29] Ware, C. (2004). Information Visualization: Perception for Design, 2nd Edition, Morgan Kaufmann, Waltham, MA.
  • [30] Warren, J. (2003). On the uniqueness of barycentric coordinates, in R. Goldman and R. Krasauskas (Eds.), Topics in Algebraic Geometry and Geometric Modeling, Contemporary Mathematics, Vol. 334, American Mathematical Society, Providence, RI, USA, pp. 93–99.
  • [31] Zhou, Y., Wischgoll, T., Blaha, L.M., Smith, R. and Vickery, R.J. (2014). Visualizing confusion matrices for multidimensional signal detection correlational methods, Proceedings of the SPIE Conference on Visualization and Data Analysis, San Francisco, CA.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-61ad4b88-8be2-444e-87ca-3c495b0d3842
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.