Formal Framework for Data Mining with Association Rules and Domain Knowledge : Overview of an Approach

• Autorzy: Rauch J.

Identyfikatory

DOI

10.3233/FI-2015-1175

• Języki publikacji: EN

Abstrakty

EN

A formal framework for data mining with association rules is introduced. The framework is based on a logical calculus of association rules which is enhanced by several formal tools. The enhancement allows the description of the whole data mining process, including formulation of analytical questions, application of an analytical procedure and interpretation of its results. The role of formalized domain knowledge is discussed.

Słowa kluczowe

EN

data mining; association rules; formalization of CRISP-DM; domain knowledge; logical calculus of association rules

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2015
• Tom: Vol. 137, nr 2
• Strony: 171--217
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Rauch J.

• University of Economics, Prague n´am. W. Churchilla 4, 130 67 Prague 3, Czech Republic

Bibliografia

• [1] Agrawal, R., Imielinski, T., Swami, A.: Mining Associations between Sets of Items in Large Databases, Proc. 1993 ACM SIGMOD International Conference on Management of Data (P. Buneman, S. Jajodia, Eds.), ACM Press, Fort Collins, 1993.
• [2] Brossette, S.E., Sprague, A.P., Hardin, J.M., Waites, K.B., Jones, W.T., Moser, S.A.: Association rules and data mining in hospital infection control and public health surveillance, Journal of the American Medical Informatics Association (JAMIA), 5(4), 1998, 373–381.
• [3] Delgado, M., Sanchez, D, Martin-Bautista, M.J., Vila, M.A.: Mining association rules with improved semantics in medical databases, Artificial Intelligence in Medicine, 21(1–3), 2001, 241–245.
• [4] Delgado, M., Ruiz, M.D., Sanchez, D.: New Approaches for Discovering Exception and Anomalous Rules, International Journal of Uncertainty and Knowledge-based Systems, 19(2), 2011, 361–399.
• [5] Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A survey, ACM Computing Surveys (CSUR), 38(3), 2006, 1–32.
• [6] Hájek P., Havránek T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory, Springer, Berlin Heidelberg New York, 1978.
• [7] Hájek, P., Havránek, T., Chytil, M.: GUHA Method (in Czech), Academia, Prague, 1983.
• [8] Hájek, P., Sochorová, A., Zvárová, J.: GUHA for personal computers, Computational Statistics & Data Analysis, 19(2), 1995, 149–153.
• [9] Hájek, P., Holeňa, M., Rauch, J.: The GUHA method and its meaning for data mining, Journal of Computer and System Sciences, 76(1), 2010, 34–48.
• [10] Havránek, T.: The present state of the GUHA software, International Journal of Man-Machine Studies, 15(3), 1981, 253–264.
• [11] Mansingh, G., Osei-Bryson, K.-M., Reichgelt. H.: Using ontologies to facilitate post-processing of association rules by domain experts, Information Sciences, 181(3), 2011, 419–434.
• [12] Ordonez, C., Ezquerra, N., Santana, C.A.: Constraining and Summarizing Association Rules in Medical Data, Knowledge and Information Systems (KAIS), 9(3), 2006, 259–283.
• [13] Piché, R., Jarvepaa, M., Turunen, E., Šimůnek, M.: Bayesian analysis of GUHA hypotheses, Journal of Intelligent Information Systems, 42(1), 2014, 47–73.
• [14] Ralbovsky, M., Kuchař, T.: Using Disjunctions in Association Mining, Proc. Advances in Data Mining - Theoretical Aspects and Applications (P. Perner, Ed.), LNCS 4597, Springer-Verlag, Berlin, 2007.
• [15] Rauch, J.: Some Remarks on Computer Realizations of GUHA Procedures, International Journal of Man-Machine Studies, 10(1), 1978, 23–28.
• [16] Rauch J.: Logic of Association Rules, Applied Intelligence, 22(1), 2005, 9–28.
• [17] Rauch, J.: Considerations on Logical Calculi for Dealing with Knowledge in Data Mining, in: Advances in Data Management (Z.W. Ras, A. Dardzinska, Eds.), Springer-Verlag, 2009, 177–199.
• [18] Rauch, J.: Logical Aspects of the Measures of Interestingness of Association Rules, in: Advances in Machine Learning II (J. Koronacki et al., Eds.), Springer-Verlag, 2010, 175 – 203.
• [19] Rauch, J.: Consideration on a Formal Frame for Data Mining, Proc. Granular Computing (GrC), 2011 (T. Hong et al., Eds.), IEEE Computer Society, Piscataway, 2011.
• [20] Rauch, J.: Formalizing Data Mining with Association Rules, Proc. Granular Computing (GrC), 2012, IEEE Computer Society, Los Alamitos, 2012.
• [21] Rauch, J.: Domain Knowledge and Data Mining with Association Rules - a Logical Point of View, Proc. Foundations of Intelligent Systems (L. Chen et al., Eds.), LNCS 7661, Springer-Verlag, Berlin, 2012.
• [22] Rauch, J.: EverMiner – Consideration on Knowledge Driven Permanent Data Mining Process, International Journal of Data Mining, Modelling and Management, 4(4), 2012, 224–243
• [23] Rauch, J.: Observational Calculi and Association Rules, Springer-Verlag, Berlin, 2013.
• [24] Rauch, J., Šimůnek, M.: Mining for 4ft Rules, Proc. Discovery Science, Third International Conference (S. Arikawa, S., Morishita, Eds.), LNCS 1967, Springer-Verlag, Berlin, 2000.
• [25] Rauch, J., Šimůnek, M.: An Alternative Approach to Mining Association Rules, in: Data Mining: Foundations, Methods, and Applications (T.Y. Lin et al., Eds.), Springer-Verlag, 2005, 219–238.
• [26] Rauch, J. Šimůnek, M.: GUHA Method and Granular Computing, Proc. 2005 IEEE International Conference on Granular Computing, (X., Hu, et al., Eds.), IEEE Computer Society, 2005.
• [27] Rauch J., Šimůnek, M. LAREDAM - Considerations on System of Local Analytical Reports from Data Mining, Proc. Foundations of Intelligent Systems (A. An et al., Eds.), LNCS 4994, Springer-Verlag, Berlin, 2008.
• [28] Rauch, J., Šimůnek, M. Dealing with Background Knowledge in the SEWEBAR Project, in Knowledge Discovery Enhanced with Semantic and Social Information (B. Berendt, et al., Eds.), Springer-Verlag, Berlin, 2009, 89 – 106
• [29] Rauch J., Šimůnek, M.: Action Rules and the GUHA Method: Preliminary Considerations and Results, Proc. Foundations of Intelligent Systems (J. Rauch et al., Eds.), LNCS 5722, Springer-Verlag, Berlin, 2009.
• [30] Rauch J., Šimůnek M.: Applying Domain Knowledge in AssociationRules Mining Process - First Experience, Proc. Foundations of Intelligent Systems (M. Kryszkiewicz et al., Eds.), LNCS 6804, Springer-Verlag, Berlin, 2011.
• [31] Roddick, J.F., Fule, P., Graco, W.J.: Exploratory Medical Knowledge Discovery: Experiences and Issues, SIGKDD Explorations, 5(1), 2003, 94–99.
• [32] Romei, A., Turini, F.: Inductive database languages: requirements and examples. Knowledge and Information Systems, 26(3), 2011, 351–384.
• [33] Suzuki, E.: Undirected Discovery of Interesting Exception Rules, International Journal of Pattern Recognition and Artificial Intelligence, 16(8), 2002, 1065–1086.
• [34] Šimůnek, M.: Academic KDD Project LISp-Miner, in Advances in Soft Computing - Intelligent Systems Design and Applications, (A. Abraham et al., Eds.), Springer-Verlag, Berlin, 2003, 263–272.
• [35] Šimůnek, M., Tammisto, T.: Distributed Data-Mining in the LISp-Miner System Using Techila Grid, Proc. Networked Digital Technologies - Second International Conference (F. Zavoral et al., Eds.), Springer-Verlag, Berlin, 2010.
• [36] Šimůnek, M., Rauch J.: EverMiner – Towards Fully Automated KDD Process, in New Fundamental Technologies in Data Mining Funatsu (K. Funatsu, K. Hasegava, Eds.), InTech Rijeka, 2011, 221–240.
• [37] Qiang Y., Xindong W.: 10 Challenging Problems in Data Mining Research, International Journal of Information Technology & Decision Making, 5(4), 2006, 597–604.