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Języki publikacji
Abstrakty
We are investigating the ability to data mine fuzzy tuples, which are often utilized to represent uncertainty about the registered information. We discuss different aspects of fuzzy databases and comment on practical advantages of the model we utilized in our research. Motivated by a well known technique called Attribute-Oriented Induction, which has been developed for summarization of ordinary relational databases, we propose a new heuristic algorithm, allowing attribute-oriented defuzzification of fuzzy database tuples to the form acceptable for many regular (i.e. atomic values based) data mining algorithms. Significant advantages of our approach to defuzzification of fuzzy database tuples include: (1) its intuitive character of fuzzy tuples' interpretation, (2) a unique capability of incorporating background knowledge, implicitly stored in the fuzzy database models in the form of fuzzy similarity relation, directly into the imprecise data interpretation process, (3) transformation of fuzzy tuples to a format easy to process by regular data mining algorithms, and (4) a good scalability for time-efficient treatment of large datasets containing non-atomic, categorical data entries.
Czasopismo
Rocznik
Tom
Strony
419--453
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
autor
- Department of Computer Science, Montana State University, Bozeman, MT 59717-3880, USA, angryk@cs.montana.edu
Bibliografia
- ANGRYK, R. (2006) Similarity-driven Denazification of Fuzzy Tuples for Entropy-based Data Classification Purposes. Proc. 15th IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE'06), Vancouver, Canada, July 2006, 1490-1498.
- ANGRYK, R. and PETRY, F. (2007) Discovery of generalized knowledge from Proximity-and Similarity-based Fuzzy Relational Databases. International Journal of Intelligent Systems 22 (7), 763-779.
- ANGRYK, R. and PETRY, F. (2005) Mining Multi-Level Associations with Fuzzy Hierarchies. Proc. 14th IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE ‘05), Reno, NV, USA, May 2005, 785-790.
- BALDWIN, J.F. and ZHOU, S.Q. (1984) Fuzzy relational inference language. Fuzzy Sets and Systems 14 (2), 155-174.
- BUCKLES, B.P. and PETRY, F.E. (1982) A fuzzy representation of data for relational databases. Fuzzy Sets and Systems 7 (3), 213-226.
- BUCKLES, B.P. and PETRY, F.E. (1983) Information-theoretic characterization of fuzzy relational databases. IEEE Transactions on Systems, Man, and Cybernetics 13 (1), 74-77.
- CHUANG, S.-L. and CHIEN, L.-F. (2002) Towards automatic generation of query taxonomy: a hierarchical query clustering approach. Proc. 2nd IEEE Int. Conf. Data Mining (ICDM-IEEE ‘02), Maebashi City, Japan, December 2002, 75-82.
- CHUANG, S.-L. and CHIEN, L.-F. (2004) A practical Web-based approach to generating topic hierarchy for text segments. Proc. of Conf. Information and Knowledge Management (CIKM’04), Washington, DC, November 2004, 127-136.
- CODD, E.F. (1970) A Relational Model of Data for Large Shared Data Banks. Communications of the ACM 13 (6), 377-387.
- HAN, J., CAI, Y. and CERCONE, N. (1992) Knowledge discovery in databases: An attribute-oriented approach. Proc. 18th Int. Conf. Very Large Data Bases (VLDB ‘92), Vancouver, Canada, 547-559.
- HAN, J., CAI, Y. and CERCONE, N. (1993) Data-Driven Discovery of Quantitative Rules in Relational Databases. IEEE Transactions on Knowledge and Data Engineering 5 (1), 29-40.
- HAN, J. and KAMBER, M. (2006) Data Mining: Concepts and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco, CA.
- KACPRZYK, J. and ZADROZNY, S. (2005) Linguistic database summaries and their protoforms: towards natural language based knowledge discovery tools. Information Sciences 173 (4), 281-304.
- KANTARDZIC, M. and ZURADA, J. (2005) New Generation of Data Mining Applications. IEEE Press and John Wiley.
- MEDINA, J.M., PONS, O. and VILA, M.A. (1994) GEFRED: a generalized model of fuzzy relational databases. Information Sciences-Informatics and Computer Science: An International Journal 76 (1-2), 87-109.
- PETRY, F.E. (1996) Fuzzy Databases: Principles and Applications. Kluwer Academic Publishers, Boston, MA.
- PRADE, H. and TESTEMALE, C. (1984) Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries. Information Sciences 34 (2), 115-143.
- RAJU, K.V.S.V.N. and MAJUMDAR, A.K. (1988) Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems. ACM Transactions on Database Systems 13 (2), 129-166.
- RUNDENSTEINER, E.A., HAWKES, L.W. and HANDLER, W. (1989) On nearness measures in fuzzy relational data models. International Journal of Approximate Reasoning 3 (3), 267-298.
- SHENOI, S. and MELTON, A. (1989) Proximity Relations in the Fuzzy Relational Database Model. International Journal of Fuzzy Sets and Systems 31 (3), 285-296.
- TAMURA, S., HIGUCHI, S. and TANAKA, K. (1971) Pattern Classification Based on Fuzzy Relations. IEEE Transactions on Systems, Man, and Cybernetics 1 (1), 61-66.
- URRUTIA, A., GALINDO, J., JIMENZ, L. and PIATTINI, M. (2006) Data Modeling Dealing With Uncertainty in Fuzzy Logic. In: D. Avison, S. Elliot, J. Krogstie, J. Pries-Heje, eds., The Past and Future of Information Systems: 1976-2006 and Beyond. Series: IF IP International Federation for Information Processing 214, Springer, Boston, 201-217.
- WALL, B., RICHTER, N. and ANGRYK, R. (2005) Creating Concept Hierarchies in an Information Retrieval System. Proc. 5th IEEE Int. Conf. Data Mining (ICDM-IEEE ‘05), Workshop on Foundations of Semantic Oriented Data and Web Mining, Houston, TX, USA, November 2005, 99-105.
- WEKA 3 (2009) Data Mining Software in Java (March 17th, 2009), http://www.cs.waikato.ac.nz/ml/weka/
- YAGER, R.R. and PETRY, F.E. (2006) A Multicriteria Approach to Data Summarization Using Concept Ontologies. IEEE Transactions on Systems, Man, and Cybernetics 14 (6), 767-780.
- ZADEH. L.A. (1970) Similarity relations and fuzzy orderings. Information Sciences 3 (2), 177-200.
- ZEMANKOVA-LEECH, M. and KANDEL, A. (1984) Fuzzy Relational Databases - a Key to Expert Systems. Interdisciplinary Systems Research, Verlag TUV, Rheinland, Koln.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0040-0005