PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Tree-based Construction of Low-cost Action Rules

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A rule is actionable, if a user can do an action to his/her advantage based on that rule. Actionability can be expressed in terms of attributes that are present in a database. Action rules are constructed from certain pairs of classification rules, previously extracted from the same database, each defining a preferable decision class. It is assumed that attributes are divided into two groups: stable and flexible. Flexible attributes provide a tool for making hints to a user to what changes within some values of flexible attributes are needed to re-classify a group of objects, supporting the action rule, from one decision class to another, more desirable, one. Changes of values of some flexible attributes can be more expensive than changes of other values. To investigate such cases, the notion of a cost is introduced and it is assigned by an expert to each such a change. Action rules construction involves both flexible and stable attributes listed in certain pairs of classification rules. The values of stable attributes are used to create action forest. We propose a new strategy which combines the action forest algorithm of extracting action rules and a heuristic strategy for generating reclassification rules of the lowest cost. This new strategy presents an enhancement to both methods.
Słowa kluczowe
Wydawca
Rocznik
Strony
213--225
Opis fizyczny
bibliogr. 19 poz., wykr.
Twórcy
autor
  • Dept. of Electronics, Computer, & Information Technology School of Technology, North Carolina A&T State University Greensboro, NC, USA, lishiangtsay@yahoo.com
Bibliografia
  • [1] Adomavicius G, Tuzhilin A (1997) Discovery of actionable patterns in databases: the action hierarchy approach. In: Proceedings of KDD97 Conference. Newport Beach, CA. AAAI Press
  • [2] Chmielewski M R, Grzymala-Busse J W, Peterson N W, Than S (1993) The rule induction system LERS - a version for personal computers. In: Foundations of Computing and Decision Sciences. Vol. 18, No. 3-4, Institute of Computing Science, Technical University of Poznan, Poland: 181-212
  • [3] Dardzi'nska A, Ra's ZW(2003) On Rule Discovery from Incomplete Information Systems. In Proceedings of ICDM'03Workshop on Foundations and New Directions of Data Mining, (Eds: T.Y. Lin, X. Hu, S. Ohsuga, C. Liau). Melbourne, Florida, IEEE Computer Society, 31-35
  • [4] Geffner H, Wainer J (1998) Modeling action, knowledge and control. In: ECAI 98, Proceedings of the 13th European Conference on AI, (Ed. H. Prade). JohnWiley & Sons, 532-536
  • [5] Greco S,Matarazzo B, Pappalardo N, Slowinski R (2005)Measuring expected effects of interventions based on decision rules. In: Special Issue on Knowledge Discovery, (Ed. Z.W. Ra's). Journal of Experimental and Theoretical Artificial Intelligence. Taylor and Francis, Vol. 17, No. 1-2, 103-118
  • [6] Grzymala-Busse J (1997) A new version of the rule induction system LERS. In: Fundamenta Informaticae, Vol. 31, No. 1, 27-39
  • [7] Liu B, Hsu W, Chen S (1997) Using general impressions to analyze discovered classification rules. In: Proceedings of KDD97 Conference, Newport Beach, CA, AAAI Press
  • [8] Pawlak Z (1991) Rough sets-theoretical aspects of reasoning about data. Kluwer, Dordrecht
  • [9] Pawlak Z (1981) Information systems - theoretical foundations. In: Information Systems Journal, Vol. 6, 205-218
  • [10] Polkowski L, Skowron A (1998) Rough sets in knowledge discovery. In: Studies in Fuzziness and Soft Computing, Physica-Verlag, Springer
  • [11] Ra's Z, Wieczorkowska A (2000) Action rules: how to increase profit of a company. In: Principles of Data Mining and Knowledge Discovery, (Eds. D.A. Zighed, J. Komorowski, J. Zytkow), Proceedings of PKDD'00, Lyon, France, LNCS/LNAI, No. 1910, Springer-Verlag, 587-592
  • [12] Ra's Z W, Tsay L-S (2003) Discovering extended action-rules (System DEAR). In: Intelligent Information Systems 2003, Proceedings of the IIS'2003 Symposium, Zakopane, Poland, Advances in Soft Computing, Springer-Verlag, 293-300
  • [13] Ra's Z, Gupta S (2002) Global action rules in distributed knowledge systems. In: Fundamenta Informaticae Journal, IOS Press, Vol. 51, No. 1-2, 175-184
  • [14] Silberschatz A, Tuzhilin A (1995) On subjective measures of interestingness in knowledge discovery. In: Proceedings of KDD'95 Conference, AAAI Press
  • [15] Silberschatz A, Tuzhilin A (1996) What makes patterns interesting in knowledge discovery systems. In: IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6
  • [16] Tsay L-S, Ra's Z.W (2005) Action rules discovery: System DEAR2, method and experiments. In: Special Issue on Knowledge Discovery, (Ed. Z.W. Ra's). Journal of Experimental and Theoretical Artificial Intelligence. Taylor and Francis, Vol. 17, No. 1-2, 119-128
  • [17] Tsay L-S, Ra's Z.W., Wieczorkowska A (2004) Tree-based algorithm for discovering extended action-rules (System DEAR2). In: Intelligent Information Systems 2004, Advances in Soft Computing, Proceedings of the IIS'2004 Symposium, Zakopane, Poland, Springer-Verlag, 2004, 459-464
  • [18] Tsay L-S, Ra's Z.W (2008) E-Action Rules. in: Data Mining: Foundations and Practice. Studies in Computational Intelligence, T.Y. Lin et al. (Eds.), Vol. 118, Springer, 2008, will appear
  • [19] Tzacheva A, Ra's Z.W (2005) Action rules mining. International Journal of Intelligent Systems. Wiley, Vol. 20, No. 7, 719-736
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0018-0012
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.