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Probabilistic relational data mining

Published: 26 February 2010 Publication History

Abstract

Probabilistic Relational Data Mining (PRDM) refers to the use of Knowledge discovery in database (KDD) methods of learning probabilistic statistical models from relational data that has information about several types of objects. This is usually the case when the database has more than one table. PRDM provides techniques for discovering descriptive models, including relationships, correlations and causal dependencies, embedded in a set of objects as well as their component attributes. In essence, it is a marriage of probabilistic modeling, multi-relational database modeling, and object oriented modeling. The three modeling processes are integrated together into a data mining system to fulfill the overall modeling task, in which, intuitively speaking, database modeling plays a role of input, probabilistic modeling is like an output, and object-oriented modeling provides necessary background information.

References

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Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., and Wirth, R. (2000). CRISP-DM 1.0 {Step-by-Step Data Mining Guide. Online Report by SPSS, NCR and Daimler-Benz.
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Connolly, T. M. and Begg, C. E. (2002). Database Systems: A Practical Approach to Design Implementation, and Management. Harlow: Addison-Wesley, third edition. International computer science series
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Codd, E. F. (1970). A relational model of data for large shared data banks. Comm. ACM, 13(6):377--387
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Codd, E. F. (1990). The Relational Model for Database Management: Version 2. Addison - Wesley
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D. Heckerman. A Tutorial on Learning with Bayesian Networks
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Elmasri, R. and Navathe, S. B. (1997).Fundamentals of Database Systems. Addison-Wesley, third edition. World Student Series
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Eriksson, H.-E. and Penker, M. (1998). UML Toolkit. John Wiley & Sons, Inc. New York
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Khoshafian, S. and Abnous, R. (1990). Object Orientation: Concepts, Databases, User Interfaces. John Wiley & Sons, Inc. New York
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Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, Dept. Computer Science, UC Berkeley

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  1. Probabilistic relational data mining

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    ICWET '10: Proceedings of the International Conference and Workshop on Emerging Trends in Technology
    February 2010
    1070 pages
    ISBN:9781605588124
    DOI:10.1145/1741906
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • UNITECH: Unitech Engineers, India
    • AICTE: All India Council for Technical Education

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    New York, NY, United States

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    Published: 26 February 2010

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    Author Tags

    1. aggregation
    2. attributes
    3. classification
    4. data model
    5. entity relationship
    6. instantiation
    7. multi-relational data
    8. object identifier
    9. object oriented models
    10. tuples

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