Properties of a Granular Computing Framework for Mining Relational Data

This work investigates properties of a framework for mining relational data. The framework is constructed based on granular computing theory and is equipped with a method for deriving information granules from relational data. Such granules are the basis for discovering knowledge of a different type. It is shown in the paper that thanks to the properties one can improve the performance of tasks such as relational objects representation, search space limitation, and relational patterns generation.

[1]  Jingtao Yao,et al.  Information granulation and granular relationships , 2005, 2005 IEEE International Conference on Granular Computing.

[2]  Hendrik Blockeel,et al.  Multi-Relational Data Mining, Using UML for ILP , 2000, PKDD.

[3]  Andrzej Bargiela,et al.  Toward a Theory of Granular Computing for Human-Centered Information Processing , 2008, IEEE Transactions on Fuzzy Systems.

[4]  Tsau Young Lin,et al.  Special issue on granular computing and data mining , 2004, Int. J. Intell. Syst..

[5]  Gordon Plotkin,et al.  A Note on Inductive Generalization , 2008 .

[6]  J. Stepaniuk.  Rough – Granular Computing in Knowledge Discovery and Data Mining , 2008 .

[7]  Nada Lavrač,et al.  An Introduction to Inductive Logic Programming , 2001 .

[8]  Tsau Young Lin,et al.  Introduction to special issues on data mining and granular computing , 2005, International Journal of Approximate Reasoning.

[9]  Piotr Honko,et al.  Granular computing for relational data classification , 2013, Journal of Intelligent Information Systems.

[10]  Piotr Honko,et al.  Association discovery from relational data via granular computing , 2013, Inf. Sci..

[11]  Y. Yao.  Granular Computing : basic issues and possible solutions , 2000 .

[12]  L. D. Raedt,et al.  Three companions for data mining in first order logic , 2001 .

[13]  Tsau Young Lin,et al.  Granular Computing , 2003, RSFDGrC.

[14]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[15]  Theo Tryfonas,et al.  Frontiers in Artificial Intelligence and Applications , 2009 .

[16]  Piotr Honko,et al.  Similarity-Based Classification in Relational Databases , 2010, Fundam. Informaticae.

[17]  Hendrik Blockeel,et al.  Multi-Relational Data Mining , 2005, Frontiers in Artificial Intelligence and Applications.

[18]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[19]  Andrzej Skowron,et al.  Information granules: Towards foundations of granular computing , 2001, Int. J. Intell. Syst..

[20]  Saěso Dězeroski.  Relational Data Mining , 2001, Encyclopedia of Machine Learning and Data Mining.

[21]  Vladik Kreinovich,et al.  Handbook of Granular Computing , 2008 .