Granular Computing: A Problem Solving Paradigm

Granulation is a natural problem-solving methodology deeply rooted in human thinking; it is intrinsically fuzzy, vague and imprecise. Mathematicians idealized it to partition, and developed into a fundamental problem solving methodology. Granulation and partition are examined in parallel from the prospect of problem solving. In partition theory, knowledge processing are transformed into table or tree processing. For general granulation such transformation is not there. In this paper, we take a new fresh look at previous results (Lin, 1998a), including the recent applications in computer security (Chinese Wall Security Policy model) from this prospect; the knowledge processing have been transformed to table and tree processing in the (pre-)topological setting

[1]  Lotfi A. Zadeh,et al.  Fuzzy Algorithms , 1968, Inf. Control..

[2]  Tsau Young Lin,et al.  Granular Computing: Fuzzy Logic and Rough Sets , 1999 .

[3]  Michael J. Nash,et al.  The Chinese Wall security policy , 1989, Proceedings. 1989 IEEE Symposium on Security and Privacy.

[4]  Lotfi A. Zadeh,et al.  Some reflections on information granulation and its centrality in granular computing, computing with words, the computational theory of perceptions and precisiated natural language , 2002 .

[5]  Yiyu Yao,et al.  A Partition Model of Granular Computing , 2004, Trans. Rough Sets.

[6]  Qiming Chen,et al.  Neighborhood and associative query answering , 2004, Journal of Intelligent Information Systems.

[7]  Eugene Wong,et al.  Canonical structure in attribute based file organization , 1971, CACM.

[8]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .

[9]  Ian Witten,et al.  Data Mining , 2000 .

[10]  Frank Harary,et al.  A formal system for information retrieval from files , 1970, Commun. ACM.

[11]  E. H. Mamdani,et al.  Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis , 1976, IEEE Transactions on Computers.

[12]  Qing Liu.  Granular Language and Its Applications in Problem Solving , 2004, Rough Sets and Current Trends in Computing.

[13]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[14]  T. T. Lee.  An algebraic theory of relational databases , 1983, The Bell System Technical Journal.

[15]  Yiyu Yao,et al.  Level Construction of Decision Trees in a Partition-based Framework for Classi cation , 2004, SEKE.

[16]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[17]  Tsau Young Lin,et al.  Chinese wall security policy-an aggressive model , 1989, [1989 Proceedings] Fifth Annual Computer Security Applications Conference.

[18]  Tsan-sheng Hsu,et al.  Medical privacy protection based on granular computing , 2004, Artif. Intell. Medicine.

[19]  Avron Barr,et al.  The Handbook of Artificial Intelligence , 1982 .

[20]  하수철,et al.  [서평]「Component Software」 - Beyond Object-Oriented Programming - , 2000 .

[21]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[22]  Tsau Young Lin,et al.  Chinese Wall Security Policy Models: Information Flows and Confining Trojan Horses , 2003, DBSec.

[23]  Masahiro Inuiguchi,et al.  New Fuzzy Rough Sets Based on Certainty Qualification , 2004, Rough-Neural Computing: Techniques for Computing with Words.

[24]  Kenneth P. Bogart,et al.  Introductory Combinatorics , 1977 .

[25]  Jerzy W. Grzymala-Busse,et al.  Data with Missing Attribute Values: Generalization of Indiscernibility Relation and Rule Induction , 2004, Trans. Rough Sets.

[26]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[27]  Tsau Young Lin,et al.  Data Mining and Machine Oriented Modeling: A Granular Computing Approach , 2000, Applied Intelligence.

[28]  Barr and Feigenbaum Edward A. Avron,et al.  The Handbook of Artificial Intelligence , 1981 .

[29]  Ravi S. Sandhu,et al.  Configuring role-based access control to enforce mandatory and discretionary access control policies , 2000, TSEC.

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

[31]  Bo Zhang,et al.  Theory and Applications of Problem Solving , 1992 .

[32]  Garrett Birkhoff,et al.  A survey of modern algebra , 1942 .

[33]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[34]  Andrzej Skowron,et al.  Rough-Neural Computing: Techniques for Computing with Words , 2004, Cognitive Technologies.

[35]  S. Miyamoto.  Generalizations of multisets and rough approximations , 2004 .

[36]  T. Y. Lin,et al.  Neighborhood systems and relational databases , 1988, CSC '88.

[37]  Tsau Young Lin,et al.  Topological and Fuzzy Rough Sets , 1992, Intelligent Decision Support.

[38]  Clemens A. Szyperski,et al.  Component software - beyond object-oriented programming , 2002 .

[39]  T. Y. Lin,et al.  NEIGHBORHOOD SYSTEMS AND APPROXIMATION IN RELATIONAL DATABASES AND KNOWLEDGE BASES , 1989 .

[40]  T. Y. Lin,et al.  Granular Computing on Binary Relations II Rough Set Representations and Belief Functions , 1998 .